OA16556A - Method and systems for reservoir modeling, evaluation and simulation. - Google Patents

Method and systems for reservoir modeling, evaluation and simulation. Download PDF

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OA16556A
OA16556A OA1201300376 OA16556A OA 16556 A OA16556 A OA 16556A OA 1201300376 OA1201300376 OA 1201300376 OA 16556 A OA16556 A OA 16556A
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OAPI
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formation
fluid
model
réservoir
properties
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OA1201300376
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Andrew W. POMERANTZ
Youxiang Zuo
John WAGGONER
Zulfiquar ALI REZA
Sophie Nazik Godefroy
Thomas Pfeiffer
Denise E. FREED
Oliver C. Mullins
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Logined B.V.
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Abstract

Fluid property modeling that employs a model that characterizes asphaltene concentration gradients is integrated into a reservoir modeling and simulation framework to allow for reservoir compartmentalization (the presence or absence of flow barrier in the reservoir) to be assessed more quickly and easily. Additionally, automated integration of the fluid property modeling into the reservoir modeling and simulation framework allows the compositional gradients produced by the fluid property modeler (particularly asphaltene concentration gradients) to be combined with other data, such as geologic data and other petrophysical data, which allows for more accurate assessment of reservoir compartmentalization.

Description

METHOD AND SYSTEMS FOR RESERVOIR
MODELING, EVALUATION AND SIMULATION
FIELD OF THE INVENTION
This invention relates to methods and apparatus for modeling, evaluating and simulating hydrocarbon bearing subterranean formations (which are commonly referred to as réservoirs).
STATE OF THE ART
Petroleum consists of a complex mixture of hydrocarbons of various molecular weights, plus other organic compounds. The exact molecular composition of petroleum varies widely from formation to formation. The proportion of hydrocarbons in the mixture is highly variable and ranges from as much as 97% by weight in the lighter oils to as little as 50% in the heavier oils and bitumens. The hydrocarbons in petroleum are mostly alkanes (linear or branched), cycloalkanes, aromatic hydrocarbons, or more complicated chemicals like asphaltenes. The other organic compounds in petroleum typically contain carbon dioxide (CO2), nitrogen, oxygen and sulfur, and trace amounts of metals such as iron, nickel, copper and vanadium.
Petroleum is usually characterized by SARA fractionation where asphaltenes are removed by précipitation with a paraffinic solvent and the deasphalted oil separated into saturâtes, aromatics and resins by chromatographie séparation.
The saturâtes include alkanes and cycloalkanes. The alkanes, also known as parafons, are saturated hydrocarbons with straight or branched chains which contain only carbon and hydrogen and have the general formula CnH2n*2. They generally have from 5 to 40 carbon atoms per molécule, although trace amounts of shorter or longer molécules may be présent in the mixture. The alkanes include methane (CH4), ethane (C2H0), propane (C3He), i-butane (iC^H10), n-butane (nC4H10), i-pentane (iC5H12), n-pentane (nC5H12), hexane (C6H14), heptane (C7H16), octane (CeH18). nonane (C9H20), decane (C10H22), hendecane (CnH24) - also referred to as endecane or undecane, dodecane (C12H26), tridecane (C13H28), tetradecane (Ci4H30), pentadecane (Ci5H32) and hexadecane (C16H34). The cycloalkanes, also known as napthenes, are saturated hydrocarbons which have one or more carbon rings to which hydrogen atoms are attached according to the formula CnH2n. Cycloalkanes have similar properties to alkanes but have higher boiling points. The cycloalkanes include cyclopropane (C3H6), cyclobutane (C4Hfl), cyclopentane (C5H10), cyclohexane (C6H12), cycloheptane (C7H14), etc.
The aromatic hydrocarbons are unsaturated hydrocarbons which have one or more planar sixcarbon rings called benzene rings, to which hydrogen atoms are attached with the formula
CdHn. They tend to burn with a sooty flame, and many hâve a sweet aroma. Some are carcinogenic. The aromatic hydrocarbons include benzene (C6He) and dérivatives of benzene as well as polyaromatic hydrocarbons.
Resins are the most polar and aromatic species présent in the deasphalted oil and, it has been suggested, contribute to the enhanced solubility of asphaltenes in crude oil by solvating the polar and aromatic portions of the asphaltenic molécules and aggregates.
Asphaltenes are insoluble in n-alkanes (such as n-pentane or n-heptane) and soluble in toluene. The C:H ratio is approximately 1:1.2, depending on the asphaltene source. Unlike most hydrocarbon constituants, asphaltenes typically contain a few percent of other atoms (called heteroatoms), such as sulfur, nitrogen, oxygen, vanadium and nickel. Heavy oils and tar sands contain much higher proportions of asphaltenes than do medium-API oils or Iight oils. Condensâtes are virtually devoid of asphaltenes. As far as asphaltene structure is concerned, experts agréé that some of the carbon and hydrogen atoms are bound in ring-like, aromatic groups, which also contain the heteroatoms. Alkane chains and cyclic alkanes contain the rest of the carbon and hydrogen atoms and are linked to the ring groups. Within this framework, asphaltenes exhibit a range of molecular weight and composition. Asphaltenes hâve been shown to hâve a distribution of molecular weight in the range of 300 to 1400 g/mol with an average of about 750 g/mol. This is compatible with a molécule contained seven or eight fused aromatic rings, and the range accommodâtes molécules with four to tens rings. It is also known that asphaltene molécules aggregate to form nanoaggregates and clusters.
The life cycle of a réservoir typically follows certain stages including, but not limited to: exploration, assessment, réservoir development, production, décliné, and abandonment of the réservoir. Important decisions must be made at each of these stages in order lo properly allocate resources and to assure that the réservoir meets its production potential. In the early stages of the life cycle, one begins with almost complété ignorance about the distribution of the internai properties within the réservoir. As development continues, diverse types of réservoir data are collected, such as seismic, well logs, and production data. Such réservoir data are combined to construct an understanding of the réservoir.
Computer-based software applications are commercially available for generating geological models which predict and describe the rock properties and features of subterranean formation. For example, geological models are built from data acquired during the exploration stage, such as seismic analysis, formation évaluation logs, and pressure measurements. Fluid models are built with the input from lab pressure-volume-temperature (PVT) analyses, geochemistry studies, pressure gradients, and downhole fluid analysis (DFA). Fluid models can be combined with geological models as part of a réservoir simulation grid (also commonly referred to as a réservoir model). The réservoir simulation grid represents the three-dimensional physical space of the formation by an array of discrète cells, delineated by a grid system which may be regular or irregular. Values for rock properties (e.g., porosity, permeability, water saturation) and fluid properties (e.g., compositions of liquid and gaseous phases, pressure, and température) are associated with each cell. Equations and associated computations are used to model and simulate the flow of fluids during production. Uncertainty in the values of the rock and fluid properties of the réservoir can be învestigated by constructing several different realizations of the sets of property values. The phrase réservoir characterization is sometimes used to refer to réservoir modeling activities up to the point where the réservoir simulation grid characterizes the static rock and fluid properties of the réservoir, i.e., before the simulation of the dynamic flow of fluids during production.
Such computer-based réservoir modeling applications are used to achieve a better understanding of the réservoir and make critical decisions with respect to réservoir development. However, prior to the réservoir development stage, the uncertainty in these models is relatively high. Consequently, known réservoir modeling applications are not always available with sufficient accuracy to permit efficient réservoir development. This is a problem because relatively greater risk exists in the réservoir development stage in comparison with the exploration and assessments stages. Activity tends to occur at a faster pace in the réservoir development stage. For example, an operator typically décidés which zones are to be completed immediately after logging and sampling operations. The zones are selected based on predicted commercial value as indicated by the volume of reserves represented in the existing models. If a mistake is made because of model inaccuracy, a costly workover operation and delayed production may resuit. The risks are partîcularly high in the case of offshore development because of higher development and operating costs.
One particular impediment to efficient réservoir development is réservoir compartmentalization. Réservoir compartmentalization is the natural occurrence of hydraulically isolated pockets within a réservoir. In order to produce a réservoir in an efficient manner, it is necessary to know the structure of the rock and the level of compartmentalization. A réservoir compartmenl does not produce unless it is tapped by a well. In order to justify the drilling of a well, the réservoir compartment must be sufficiently large to sustain économie production. Furthermore, in order to achieve efficient recovery, it is generally désirable to know the locations of as many of the réservoir compartments as practical before extensive development has been done.
There are three industry standard procedures widely used to understand réservoir compartmentalization. First is the évaluation of petrophysical logs. Petrophysical logs may identify imperméable barriers, and the existence of such barriers can be taken to mean that the réservoir is compartmentalized. Examples include gamma ray and NMR logs, both of which can identify imperméable barriers in favorable situations. Another example is the évaluation of mud filtrate invasion monitored by resistivity logs. However, imperméable barriers may be so thin that they are not observable by these logs, or barriers observed by these logs may not extend away from the wellbore and therefore may not compartmentalize the réservoir. Second is the évaluation of pressure gradients. If two permeable zones are not in pressure communication, they are not in flow communication. However, the presumption that pressure communication implies flow communication has repeatedly been proven to be incorrect. Pressure équilibration requires relatively little fluid flow and can occur more than 5 orders of magnitude faster than fluid compositional équilibration, even in the presence of flow barriers. Continuous pressure gradients are a necessary but insufficient test for réservoir connectivity. Third is the comparison of geochemical fingerprints of fluid samples acquired from different locations in the réservoir. Petroleum is a complex chemical mixture, containing many different chemical compounds; the composition of that petroleum can therefore be treated as a fingerprint. If the composition of petroleum samples from two different places in the réservoir is the same, it is assumed that fluids can flow readily between those two places in the resen/oir and hence that the réservoir is connected. However, forces such as biodégradation and water washing can occur to different extents in different parts of the réservoir, causing two places in the réservoir to hâve different fingerprints even if they are connected. Additionally, petroleum samples generated from the same source rock may hâve very similar fingerprints even if they corne from locations in the réservoir that are presently disconnected.
An alternative method to assess connectivity is to evaluate hydrocarbon fluid compositional grading. The chemical composition of petroleum must be different in different parts of a connected réservoir. This change in composition with position (typically depth) in the réservoir is referred to as compositional grading. The magnitude of this compositional grading (i.e., the différence in the composition of two fluids collected from different depths), in connected réservoirs at thermodynamic equilibrium, can be modeled with mathematical équations of state (EOS) and measured with downhole fluid analysis. If the magnitude of compositional grading is measured, and the measurement matches the prédictions of the model, then the assumptions of the model are believed to be correct. In this case, the assumptions are that the réservoir is connected and at thermodynamic equilibrium. In the event that the magnitude of the compositional grading does not match the prédictions of the EOS model, it can be assumed that there is réservoir compartmentalization or that the réservoir fluids are not in equilibrium.
Many different forces can contribute to a lack of thermodynamic equilibrium, such as tar mats, water washing. biodégradation, real-time charging, etc. It can be difficult to détermine whether the réservoir is compartmentalized or not in thermodynamic equilibrium, and this détermination can be critical to important development decisions. More specifically, the traditional EOS (such as the Peng-Robinson EOS developed in 1976) utilized for compositional grading analysis are derived by adding correction terms to the idéal gas law to address gas-liquid equilibria. Thus, these standard EOS allow for compositional analysis of only gas and liquid phase fractions of the réservoir fluid, and such limited information makes it difficult to détermine whether the réservoir is compartmentalized or not in thermodynamic equilibrium.
Thus, there is a clear need for méthodologies that provide for an effective understanding of réservoir compartmentalization as early as possible (e.g., before development) in the lifecycle of the réservoir.
SUMMARY
In accord with one embodiment of the invention, a method and system for réservoir modeling, évaluation and simulation is provided that allows for effective understanding of réservoir compartmentalization early in the lifecycle of the réservoir (e.g., before development).
In accord with another embodiment of the invention, fluid property modeling that employs an EOS that characterizes an asphaltene concentration gradient is incorporated into a réservoir modeling and simulation framework to allow for réservoir compartmentalization (the presence or absence of flow barriers in the réservoir) to be assessed more quickly and easily. Additionally, automated intégration of the fluid property modeling into the réservoir modeling and simulation framework allows the compositional gradients produced by the fluid property modeling (particularly the asphaltene concentration gradient) to be combined with other data, such as géologie data and other petrophysical data, which allows for more accurate assessment of réservoir compartmentalization.
In accord with another embodiment of the invention, the fluid property modeling is derived from downhole fluid analysis measurements within a wellbore that traverses the formation. The fluid property modeling may characterize asphaltene concentration as a function of location in the formation from downhole fluid color measurements (such as from an empirical relation of the form ODdfa = C1‘Wa + C2, where ODDfa is the measured color (i.e., optical density) of formation fluid at a particular wavelength, Wa is the corresponding mass fraction of asphaltenes, and C1 and C2 are constants.
In accord with yet another embodiment of the invention, the réservoir modeling and simulation framework dérivés a réservoir simulation model from the results of such fluid property modeling and provides for visualization of properties of a réservoir simulation model to evaluate réservoir compartmentalization. The framework may provide for visualizing the properties of the réservoir simulation model together with asphaltene concentration derived from downhole fluid measurements within a wellbore traversing the formation and possibly structural faults defined by the réservoir simulation model in order to evaluate réservoir compartmentalization.
In accord with still another embodiment of the invention, the EOS of the fluid property modeling of the framework dérivés property gradients, pressure gradients and température gradients as a function of depth in the formation. Such property gradients may include mass fractions, mole fractions, molecular weights, and spécifie gravitïes for a set of pseudocomponents of the formation fluid. The set of pseudocomponents may include a heavy pseudocomponent representing asphaltenes in the formation fluid, a second distillate pseudocomponent that represents the non-asphaltene liquid fraction of the formation fluid, and a third light pseudocomponent that represents gases in the formation fluid. The set of pseudocomponents can also represent single carbon number (SCN) components as well as other fractions of the formation fluid. The EOS may predict compositions! gradients with depth that take into account the impacts of at least one factor selected from the group consisting of gravitational forces, chemical forces, and thermal diffusion. The output of the EOS can be used to generate a profile of asphaltene pseudocomponents (e.g., nanoaggregates and larger asphaltene clusters) and corresponding aggregate size of asphaltenes as a function of location in the formation. The output of the EOS can also be used to predict gradients for at least one particular fluid property (e.g., fluid density and fluid viscosity) that relates to asphaltene content,
Additional objects and advantages of the invention will become apparent to those skilled in the art upon reference to the detailed description taken in conjunction with the provided figures.
BRIEF DESCRIPTION OF THE DRAWINGS
Fig. 1 is a schematic view of an exemplary subterranean formation of interest having a plurality of data acquisition tools disposed at various locations in the formation of interest for collecting data used in modeling and simulating properties of the formation of interest.
Fig. 2 is a functional block diagram of an exemplary réservoir modeling software framework for modeling and simulating properties of a formation of interest in accordance with the présent invention.
Fig. 3 is a functional block diagram of an exemplary computer workstation suitable for embodying the réservoir modeling software framework of Fig. 2.
Figs. 4A - 4B include flow charts of an exemplary workflow for réservoir assessment and réservoir development in accordance with a first embodiment of the présent invention; the workflow of Figs. 4A - 4B employs modeling of geological and fluid properties of a formation of interest together with réservoir simulation based thereon for optimizing réservoir assessment and/or réservoir development.
Figs. 5A - 5C include flow charts of an exemplary workflow for réservoir assessment and réservoir development in accordance with a second embodiment of the présent invention; the workflow of Figs. 5A - 5C employs modeling of geological and fluid properties of a formation of interest together with réservoir simulation based thereon for optimizing réservoir assessment and/or réservoir development.
DETAILED DESCRIPTION
Operations, such as surveying, drilling, wireline testing, planning and analysis, are typically performed to locate and sample hydrocarbons located in a subterranean formation over the lifecycle of the formation. Various aspects of such operations are shown in FIG. 1.
Seismic surveys are often performed using seismic acquisition méthodologies which employ a plurality of sensors (such as seismic scanner 302a as shown) that monitor the reflection and attention of sound vibrations directed into the earth formation. The sensors typically include a geophone-receiver that produces electrical output signais characteristic of the reflected sound vibrations. The electrical output signais are processed and converted into digital form (typically referred to as seismic data) for storage, transmission or further processing as desired, for example by data réduction.
Such seismic data may be processed and interpreted to characterize changes in anisotropic and/or elastic properties, such as velocity and density, of the geological formation at various depths. This information may be used to generate basic structural maps of the subterranean formation. Such structural maps can be analyzed to assess the underground formations and détermine the likelihood that hydrocarbons are located therein and are readily accessible. Inversion techniques can be applied to the seismic data to reflect reiiable rock and fluid properties of the formation. The inversion techniques may be pre-or post-stack, deterministic, stochastic or geostatistical, and typically includes other réservoir measurements such as well logs and cores,
The exemplary subterranean structure 304 of FIG. 1 may include several formations or layers, including, but not limited to: a shale layer (306a), a carbonate layer (306b), a shale layer (306c) and a sand layer (306d). A fault (307) extends through the layers 306a and 306b. Petroleum fluid is contained within the carbonate layer 306b. The seismic surveying tools may be adapted to dérivé a structural map of the réservoir as well as rock and fluid properties of the formation.
While a spécifie subterranean formation with spécifie geological structures is depicted, it will be appreciated that the structure may contain a variety of geological formations, sometimes having extreme complexity. In some locations, typically below the water line, fluid may occupy pore spaces of the formations.
One or more wells may extend into the subterranean formation. The wells can be provided with tools that are used to drill the well and/or analyze lhe subterranean formation and/or hydrocarbon fluids located therein for évaluation purposes. For example, a drilling tool 302b can be deployed from a drilling rig and advanced into the earth along a desired path as shown in FIG. 1. Fluid, such as drilling mud or other drilling fluids, may be pumped down the wellbore through the drilling tool and out the drilling bit. The drilling fluid flows through the annulus between the drilling tool and the wellbore and out the surface, carrying away earth loosened during drilling. The drilling fluids return the earth to the surface and seal the wall of the wellbore to prevent fluid in the surrounding earth from entering the wellbore and causing a blow out. During the drilling operation, the drilling tool may perform downhole measurements to investigate downhole conditions. The drilling tool may also be used to take core samples of the formation.
In some cases, the drilling tool can be removed and a wireline tool 302c can be deployed into the wellbore to perform core sampling or additional downhole testing (such as analysis of the properties of the formation, sampling of formation fluids, analysis of the properties of the formation fluids). The wireline tool 302c may be positioned at various depths in the wellbore to provide a survey or other information relating to the subterranean formation. The wireline tool 302c (and/or the drilling tool 302b) can perform a variety of operations, including, but not limited to: well logging operations, downhole fluid sampling, core sampling, and downhole fluid analysis.
Well logging operations measure rock and fluid properties of the formation (such as lithology, porosity, permeability, oil and water saturation, etc). Lithology represents the rock type and is typically measured by well logging operations such as naturel gamma, neutron, density, photoelectric, resistîvity and/or combinations thereof. Porosity represents the amount of pore space in the rock and is typically measured by neutron or gamma ray logging or NMR measurements. Permeability represents lhe quantity of fluid (usually hydrocarbon) that can flow from the rock as a function of time and pressure. Formation testing is so far the only direct downhole permeability measurement. In case of its absence, which is common in most cases, permeability estimation may be derived from other measurements, such as porosity, NMR, sonie, by empirical corrélations. Water saturation represents the fraction of the pore space occupied by water and is typically measured using an instrument that measures the resistivity of the rock. Oil saturation represents the fraction of the pore space occupied by oil and is typically measured by neutron iogging or dielectric scanning.
Downhole fluid sampling extract and store one or more live fluid samples within the tool.
Core sampling operations extract one or more core samples from the formation. Each core sample is isolated and identified from other core samples. There are several types of core samples that can be recovered from the wellbore, including but not limited to: full-dïameter cores, oriented cores, native state cores and sidewall cores. In an exemplary embodiment, the coring tool obtains one or more sidewall cores from the formation adjacent the wellbore. Core samples can also be acquired while the well is being drilled. Coring operations can be run in combination with other suitable Iogging operations (such as gamma ray Iogging) to correlate with openhole logs for accurate, real-time depth control of the coring points.
Downhole fluid analysis operations extract live fluid from the formation adjacent the wellbore and dérivé properties (e.g., GOR, oil-based-mud contamination, saturation pressure, live fluid density, live fluid viscosity, and compositional component concentrations, etc.) that characterize the live fluid at the pressure and température of the formation. For example, the Quicksilver probe and InSitu fluid analyzer commercially available from Schlumberger can be used to perform such downhole fluid analysis operations.
Laboratory analysis can be performed on the core samples and/or live fluid samples gathered from the réservoir. The live fluid samples may be reconditioned to the formation réservoir and pressure at the sample depth and subjected to analytical measurements (e.g., GOR, oil-basedmud contamination, fluid composition) that replicate the downhole fluid analysis measurements. The results of the laboratory measurements can be compared to the results of the corresponding downhole measurements for chain of custody vérification. In the case of vérification failure, actions can be taken to identify and correct the cause of the failure, which can arise from hardware failure of the downhole fluid analysis tool or laboratory tool, and inappropriate sampling, sample reconditioning and/or sample transfer techniques. The core sample can be analyzed in the laboratory by many different means. For example, such analysis can include bulk measurements (e.g., porosity, grain density, permeability, residual saturation, etc.) to measure properties of the core sample. In the case that the core sample includes movable hydrocarbons, hydrocarbon fluid can be extracted from the core sample by centrifuging the core sample. In the case that the core sample is non-movable bitumen, hydrocarbon fluid can be extracted from the bitumen core sample using a solvent. In either case, the composition of the extracted hydrocarbon fluid can be analyzed by geochemical analysis, which can be carried out by a variety of techniques including, but not limited to:
Gas chromatography, including, but not limited to: gas chromatography with various détection schemes (e.g., flame ionization detector, thermal conductivity detector, mass spectrometer);
Saturates-aromatics-resins-asphaltenes (SARA) analysis;
Optical spectroscopy in the ultraviolet, visible, and near-infrared régions; Infrared spectroscopy (including, but not limited to, instruments using Fourier transform);
Fluorescence spectroscopy;
Raman spectroscopy;
Liquid chromatography, including, but not limited to, various modifications (high pressure/performance, reverse phase, with mass spectrométrie détection, etc); Pyrolysis experiments with gas chromatography or other détection methods; Isotope analysis (for example performed using an isotope ratio mass spectrometer); and
Nuclear magnetic résonance (NMR) spectroscopy using various nuclei (13C, 1H, etc.).
Drilling may continue until the desired total depth is reached. Steel casing may be run into the well to a desired depth and cemented into place along the wellbore wall. A surface unit (not shown) may be used to communicate with the drilling tool 302b and/or wireline tool 302c and possibly to offsite operations. The surface unit may be capable of communicating with the drilling tool 302b and/or wireline tool 302c to send commands to the respective tool, and to receive data therefrom. The surface unit may be provided with computer facilities for receiving, storing, processing, and/or analyzing data from the réservoir. The surface unit collects data generated during the drilling or logging operation and produces data output which may be stored or transmitted. Computer facilities, such as those of the surface unit, may be positioned at various locations about the réservoir and/or al remote locations.
After the drilling operation is complété, the well may then be prepared for production. Complétions equipment may be deployed into the wellbore to complété the well in préparation for the production of hydrocarbons therethrough. Such complétions equipment can include a production tool 302d (such as a packer, artificial lift apparatus, sand control device, etc.) as shown in FIG. 1. Hydrocarbons are allowed to flow from the downhole réservoir through the complétions equipment to the surface. Production facilities positioned at surface locations may collect the hydrocarbons from the wellsite(s). Fluid drawn from the subterranean reservoir(s) passes to the production facilities via transport mechanisms, such as tubing, Various equipments may be positioned about the réservoir to monitor oilfield parameters, to manipulate the operations and/or to separate and direct fluids from the wells. Surface equipment and completion equipment may also be used to inject fluids into réservoirs, either for storage or at strategie points to enhance production of the réservoir. As fluid passes to the surface, various dynamic measurements, such as fluid flow rates, pressure and composition may be monitored. These parameters may be used to détermine various characteristics of the subterranean formation.
While only simplified wellsite configurations are shown, it will be appreciated that the réservoir may cover a portion of land, sea and/or water locations that hosts one or more wellsites. Production may also include injection wells (not shown) for added recovery. One or more gathering facilities may be operatively connected to one or more of the wellsites for selectively collecting downhole fluids from the wellsite(s).
The information generated by the operations depicted in FIG. 1 and summarized above may be used to evaluate the réservoir, and make decisions concerning development and production. Such decisions may involve well planning, well targeting, well complétions, operating levels, production rates and other operations and/or operating parameters.
Seïsmic data may be used by a geophysicist to détermine characteristics of the subterranean formations and features. Well-logging data as well as the data resulting from core analysis, laboratory fluid analysis and downhole fluid may characterize the porosity and permeability of the rock of the formation as well as viscosity, density and compositions of the fluids contained therein. Such information may be used by a geologist to détermine various characteristics of the subterranean formation. Production data, if available, may be used by a réservoir engineer to détermine fluid flow réservoir characteristics.
The information analyzed by the geophysicist, geologist and/or the réservoir engineer may be used in conjunction with one or more computer-based réservoir modeling applications that model the behavior of the geological formations, downhole réservoirs, wellbores, surface facilities as well as other portions of the operations. Examples of these réservoir modeling applications are shown in U.S. Pat. No. 5,992,519; W02004/049216; WO1999/064896; U.S. Pat. No. 6,313,837; US2003/0216897; U.S. Pat. No. 7,248,259; US2005/0149307;
US2006/0197759; U.S. Pat. No. 6,980,940; W02004/049216; US2004/0220846; and U.S. Pat, No. 6,801,197.
In another example, the information generated by the operations depicted in FIG. 1 can be used for decisions that optimize production of the réservoir, such as decisions with respect to drilling new wells, re-completing existing wells or alter wellbore production. Oilfield conditions, such as geological, geophysical and réservoir engineering characteristics may have an impact on operations, such as risk analysis, économie valuation, and mechanical considérations for the production of subsurface réservoirs. Data from one or more wellbores may be anaiyzed to plan or predict various outcomes at a given wellbore. In some cases, the data from neighboring wellbores, or wellbores with similar conditions or equipment, may be used to predict how a well will perform. There are usually a large number of variables and large quantities of data to consider in analyzing operations involving the réservoir. It is, therefore, often useful to model the behavior of the réservoir to détermine a desired course of action. During the ongoing operations, the operating parameters may need adjustment as oilfield conditions change and new information is received.
Embodiments of the présent disclosure may include the operations described above with respect to FIG. 1 as part of a workflow (FIGS. 4A - 4B) that effectively models a réservoir for evaluating and understanding réservoir compartmentalization, particularly during early stages of the lifecycle of the réservoir (such as the exploration and réservoir assessment stages). The workflow may employ a réservoir modeling software framework 100 as illustrated in FIG. 2. The software framework 100 may include a data store 102 that stores the data generated from the data gathering operations of FIG. 1 as illustrated schematically in FIG. 2. Such data can include well log data (i.e. petrophysical data), seismic analysis results, laboratory core and fluid analysis results, and downhole fluid analysis results that pertain to a spécifie formation of interest as well as historical data for other formations that are related to the formation of interest in some meaningful way. The particular data gathering operations may be dictated by a réservoir assessment plan as depicted schematically in FIG. 2. The goal of the réservoir assessment plan may be to dérivé an understanding of the structure and stratigraphy of the formation of inlerest as well as a forecast of the hydrocarbons that are contained in the formation of interest. Risk and uncertainty can be accounted for in particular tests and analyses that are part of the réservoir assessment plan, the réservoir modeling that is accomplished by the software framework 100, and the summary information and decisions that are based thereon.
A géologie modeler 104 opérâtes on the data stored in the data store 102 to generale a threedimensional geological model 106 ofthe formation of interest. The three-dimensional geological model 106 is a framework that provides a description of the structure and stratigraphy of the formation of interest. In an exemplary embodiment, the geological model
106 provides a basic description of the formation of interest in terms of dimensions and unconformities (e.g., fractures, layers and permeability barriers). The geological model may include the following information for the formation of interest:
Top réservoir surface, which can be a constant value or a complex surface interpolated from well markers and/or geophysics;
Base réservoir surface, which can be derived as an offset (constant or variable) from the top réservoir surface or a complex surface interpolated from well markers and/or geophysics; Intra-reservoir surface, as needed and similar to the top and base réservoir surfaces;
Réservoir boundaries, which can be derived from bounding faults, pinchouts, designated extent, etc.
Rock and fluid properties such as faciès (which can be derived from geostatistical modeling or object modeling) as well as porosity, permeability, relative permeabilities, water saturation, netto-gross ratio, capillary pressure (which can be derived from inversion of seismic data, core analyses and well logs and/or historical data)
The geological model 106 may be constructed of a large number of grid cells, with each grid cell typically populated with a réservoir property that includes, but is not limited to, rock type, porosity, permeability, initial interstitial fluid saturation, and relative permeability and capillary pressure fonctions. The geographical model can be derived from an intermediate model, such as a stratigraphie model, as is well known in the art. The grid cells can be structured or unstructured. Structured grid cells hâve similar shape and the same number of sides or faces. Common structured grid cells may be defined in Cartesian or radial coordinate Systems in which each cell has four sides in two dimensions or six faces in three dimensions. Unstructured grid cells may be made up of polygons (polyhedra in three dimensions) having shapes, sizes, and number of sides or faces that can vary from place to place. One type of unstructured grid cell includes the Voronoi grid cell. Each Voronoi grid cell may be associated with a node and a sériés of neighboring cells. The Voronoi grid may be locally orthogonal in a geometrical sense; that is, the cell boundaries may be normal to lines joining the nodes on the two sides of each boundary. For this reason, Voronoi grid cells may also commonly be called perpendicular bisection (PEBI) grid cells. Other types of unstructured grid cells can also be used.
Réservoir simulations may be performed with a coarser grid system as the direct use of finegrid models for réservoir simulation is not generally feasible because their fine level of detail places prohibitive demands on computational resources. Therefore, the software framework
100 employs one or more gridding and upsealing modules (two shown as 108A and 108B) that scale up the fine-grid géologie model 106 to a coarser réservoir simulation grid 110 while preserving, as much as possible, the fluid flow characteristics of the fine-grid geological model 106. The module 108A upscales a structured fine-grid geographical model 106 to the coarser réservoir simulation grid 110. Examples of suitable upsealing procedures for use in module 108A are provided in the following papers: Wen et al., Upsealing Hydraulic Conductivities in Heterogeneous Media: An Overview, Journal of Hydrology, Vol. 183 (1996) 9-32; Begg et al., Assigning Effective Values to Simulator Gridblock Parameters for Heterogeneous Réservoirs, SPE Réservoir Engineering (November 1989) 455-465; Durlofsky et al., Scale Up of Heterogeneous Three Dimensional Réservoir Descriptions, Paper SPE 30709 presented at the Annual Technical Conférence and Exhibition, Dallas, Tex. (Oct. 22-25,1995); and Li et al., Giobal Scale-up of Réservoir Model Permeability with Local Grid Refinement, Journal of Petroleum Science and Engineering, Vol. 14 (1995) 1-13. U.S. Patent No. 6,196,561 to Farmer, commonly assigned to assignée of the présent application, describes a structured gridding and upsealing methodology that can be carried out by module 108A. The module 108B upscales an unstructured fine-grid geographical model 106 to the coarser réservoir simulation grid 110. Examples of suitable upsealing procedures for use in module 108B are provided by M. Prévost et al., Unstructured 3D Gridding and Upsealing for Coarse Modeling of Geometrically Complex Réservoirs, Petroleum Geoscience, October2005, v. 11; no. 4, pgs. 339-345 as well as U.S. Patent No. 6,826,520 to Khan et al. and U.S. Patent No, 6,018,497 to Gunasekera. The résultant réservoir simulation grid 110 may be constructed from a coarse grid of cells that are associated with petrophysical properties such as porosity, permeability, initial interstitial fluid saturation, and relative permeability and capillary pressure fonctions. For a fractured réservoir, a dual-porosity model and/or a dual-permeability model can be used. Local grid refinements (a finer grid embedded inside of a coarse grid) can also be used, for example to more accurately represent the near wellbore multi-phase flow affects.
The software framework 100 may further include a fluid property modeler 112 that opérâtes on the data stored in the data store 102 to generate a fluid property model 114 that characterizes the fluid properties of the formation of interest. The fluid property modeler 112 may employ a particular équation of state model, referred to herein as the FHZ EOS, that dérivés property gradients, pressure gradients and température gradients as a fonction of depth in the formation of interest. These gradients may be incorporated as part of the fluid property model 114. The property gradients derived from the FHZ EOS may include mass fractions, mole fractions, molecular weights, and spécifie gravities for a set of pseudocomponents of the formation fluid. Such pseudocomponents may include a heavy pseudocomponent representing asphallenes in the formation fluid, a second distillate pseudocomponent that represents the non-asphaltene liquid fraction of the formation fluid, and a third light pseudocomponent that présents gases in the formation fluid. The pseudocomponents derived from the FHZ EOS can also represent single carbon number (SCN) components as well as other fractions or lumps of the formation fluid (such as a water fraction) as desired. The FHZ EOS can predict compositional gradients with depth that take into account the impacts of gravitational forces, chemical forces, thermal diffusion, etc. as taught in U.S. Patent Appl. Nos. 61/225,014 and 61/306,642. Other applications of the FHZ EOS hâve been described in U.S. Patent No. 7,822,554 and U.S. Patent Appl. Nos. 12/209,050; 12/352,369; 12/990,980; 12/483,813; 61/282,244; 61/387,066; 12/752,967; and 61/332,595. For some cases, one or more terms of the FHZ EOS dominate and the other terms can be ignored. For example, in low GOR black oils, the gravity term of the FHZ EOS dominâtes and the term related to chemical forces (solubility) and thermal diffusion (entropy) can be ignored.
The compositional gradients produced by the FHZ EOS can be used in conjunction with a Flory-Huggins solubility model to dérivé a concentration profile of asphaltene pseudocomponents (e.g., asphaltene nanoaggregates and larger asphaltene clusters) and corresponding aggregate size of asphaltenes as a function of depth in the formation of interest as taught in U.S. Patent Appl. Nos. 61/225,014; 61/306,642; and 61/332,595. This information can also be incorporated into the fluid property model 114.
The asphaltene concentration gradient can also be used to predict gradients for fluid properties (such as fluid density and fluid viscosity) that relate to asphaltene content. For predicting viscosity, the prédictions can be based on the empirical corrélation of the form proposed by Lohrenz, Bray and Clark in “Calculating Viscosity of Réservoir Fluids from their Composition, JPT, October 1964, pp 1171-117, or the empirical corrélation of the form proposed by Pedersen et al. in “Viscosity of Crude Oils,” Chemical Engineering Science Vol 39, No 6, pp 1011-1016, 1984. These fluid property gradients can also be incorporated into the fluid property model 114, In an exemplary embodiment, the FHZ EOS utilized by the fluid property modeler 112 may be tuned in accordance with laboratory fluid data or downhole fluid analysis data that is stored in the data store 102 and describes the fluids of the formation of interest. Corrections for drilling fluid contamination may be necessary. An example of such corrections is described in U.S. Patent Appl. No. 12/990,980.
The fluid property model 114 may be stored in the data store 102 and may include data that describes fluid properties as a function of location in the formation of interest. In an exemplary embodiment, the fluid property model 114 may include one or more of the following:
component mass fractions, molecular weights and critical properties (pressure, température,
I5 volume) as a function of location in the formation of interest;
component acentric factors, Z-factor, volume shift parameters, reference density;
btnary interaction coefficients; and formation volume factors, fluid density, fluid viscosity, and asphaltene concentration and aggregate sizes as a function of location in the formation of interest.
In an exemplary embodiment, a fluid property model may include parameters that represent the continuous changes in respective fluid properties as a function of position along one or more welîbores that traverse a formation of interest.
The framework 100 may further include a module 116 that maps or interpolâtes the fluid properties of the formation fluids as represented by the fluid property model 114 to the grid cells of the réservoir simulation grid 110. In an exemplary embodiment, the fluid properties for a given simulation grid cell may be interpolated from the fluid properties of the fluid property model corresponding to the nearest formation locations. Such interpolation may be carried out separately over the grid cells for each compartment of the formation. For example, consider a trend such as asphaltene concentration increasing with depth within from an initial value and rate of change within a réservoir compartment. That trend may occur, with the magnitude predicted by the EOS, but the trend may stop abruptly at the end of the compartment. Such trend parameters can be used to interpolate the asphaltene concentration over the grid cells of this compartment. In the next compartment, the trend may start over with a different initial value and a different rate of change. These different trend parameters can be used to interpolate the asphaltene concentration over the grid cells of the next compartment. In performing the interpolation, continuous changes of a respective fluid property value may be mapped into discrète values, and the cells may then be populated with such discrète values. That is to say, the smooth variation of a respective fluid property values may be binned into somethïng that looks like a stairstep variation.
The framework 100 may further include an évaluation module 118 that provides the user with the capability to review and analyze the information stored in the réservoir simulation grid 110 in order to understand the structural properties and fluid properties of the formation of interest. The évaluation module 118 can provide for rendering of 3-D représentations of properties of the formation of interest for use in full-field visualization. The évaluation module 118 can also display 2-D représentations of properties of the formation of interest, such as cross-sections and 2-D radial grid views. In an illustrative embodiment, the évaluation module 118 can be used to characterize the réservoir (i.e., evaluate the static state of the réservoir before any production) and identify, confirm or modify reserves forecasts for the formation of interest and/or any uncertainties and risk factor associated therewith. The information provided by the évaluation module 118 can be used to update the réservoir assessment plan in the event that uncertainties or risks are unacceptable or new information is gathered. Changes or additions to the tests and analyses of the assessment plan can be planned and carried out in order to acquire additional data, and the modeling and simulation operations ofthe modules ofthe framework 100 can be repeated in an attempt to seek a more certain understanding ofthe formation of interest.
When assessment is complété, a réservoir development plan can be defined. The réservoir development plan may store information for producing hydrocarbons from the formation of interest, such as the number and location of wells, the completion apparatus of wells, artificial lift mechanisms, enhanced recovery mechanisms (such as water flooding, steam injection for heavy oil, hydraulic fracturing for shale gas and the like), pipeline Systems, facilïties, and the expected production of fluids (gas, oil, water) from the formation. Details of the réservoir development plan may be input to a réservoir simulator module 120 of the framework 100. The réservoir simulator 120 may dérivé computational équations and associated time-varying data that represent the details of réservoir development plan over time. Examples of such computational équations and associated time-varying data is described in U.S. Patent Publ. No. 2010/0004914 to Lukyanoc et al., commonly assigned to assignée of the présent application. The réservoir simulator 120 may utilize the computational équations and associated time varying data representing the réservoir development plan together with the rock properties and fluid properties stored in the réservoir simulation grid 110 upon completion of réservoir characterization (or updated thereafter) to dérivé the pressure and fluid saturations (e.g., volume fractions) for each cell as well as the production of each phase (i.e., gas, oil, water) over a number of time steps.
In an exemplary embodiment, the réservoir simulator 120 carries out finite différence simulation, which is underpinned by three physical concepts: conservation of mass, isothermal fluid phase behavior, and the Darcy approximation of fluid flow through porous media. Thermal simulation (which may be used for heavy oil applications) adds conservation of energy to this list, allowing températures to change within the réservoir. The PVT properties of the oil and gas phases of the réservoir fluids of the grid may be fitted to an équation of state (EOS), as a mixture of components in order to dynamically track the movement of both phases and components in a formation of interest. Changes in saturation of three phases (gas, oil, and water) as well as pressure of each phase may be calculated in each cell at each time step. For example, declining pressure in a réservoir may resuit in gas being liberated from the oil. In another example, with increasing pressure in the réservoir (e.g., as a resuit of water or gas injection), gas may be re-dissolved into the oil phase. Details of exemplary operations for carrying out the finite différence simulation are set forth in U.S. Patent No. 6,230,101 to Wallis, commonly assigned to assignée of the présent application. Alternatively, finite element simulation techniques and/or streamline simuiation techniques can be used by the réservoir simulator 120. The EOS employed by the simulator 120 may be based on the FHZ EOS that is employed by the fluid property modeler 112 as described above. The FHZ EOS can be extended to dérivé and simulate a variety of properties of the réservoir fluid of the formation, including, but not limited to:
PVT properties (e.g., phase envelope, pressure-temperature (PT) flash, constant composition expansion (CCE), differential libération (DL), constant volume déplétion (CVD));
gas hydrate formation;
wax précipitation;
asphaltene précipitation; and sealing.
Examples of équations for extending the FHZ EOS model for predïcting gas hydrate formation are described in H.J. Ng et al., “The Measurement and Prédiction of Hydrate Formation in Liquid Hydrocarbon-Water Systems, Ind. Eng. Chem. Fund., 15, 293 (1976); H.J. Ng et al., “Hydrate Formation in Systems Containing Methane, Ethane, Propane, Carbon Dioxide or Hydrogen Sulfide in the Presence of Methanol, Fluid Phase Equil., 21, 145 (1985); H.J. Ng et al., “New Developments in the Measurement and Prédiction of Hydrate Formation for Processing Needs, International Conférence on Natural Gas Hydrates, Annals ofthe New York Academy of Sciences Vol. 715, 450-462 (1994); J.Y. Zuo et al. “Représentation of Hydrate Phase Equilibria in Aqueous Solutions of Methanol and Electrolytes Using an Equation of State, Energy and Fuels, 14, 19-24 (2000); and J.Y. Zuo et al., “A Thermodynamic Model for Gas Hydrates in the Presence of Salts and Methanol, Chem. Eng Comm., 184,175-192 (2001).
Examples of équations for extending the FHZ EOS model for predïcting wax précipitation are described in H. Alboudwarej et al., “Effective Tuning of Wax Précipitation Modeîs, 7th International Conférence on Petroleum Phase Behavior and Fouling, Asheville, North Carolina, (2006); J.Y. Zuo et al., An improved thermodynamic model for wax précipitation from petroleum fluids, Chemical Engineering Science, 56, 6941 (2001); and J.Y. Zuo et al., Wax Formation from Synthetic Oil Systems and Réservoir Fluids, 11th International Conférence on Properties and Phase Equilibria for Product and Process Design, Crete, Greece, May 20-25, (2007).
An example of équations for extending the FHZ EOS model for predicting asphaltene précipitation is described in J. Du et al., “A Thermodynamic Model for the Prédictions of Asphaltene Précipitation, Petroleum Science and Technology, 22, 1023 (2004).
The évaluation module 118 can provide for construction of 3-D représentations of the properties of the formation of interest over time as output by the simulator 120 for use in full-field évaluation. The évaluation module 118 can also provide 2-D représentations of properties of the formation of interest over time as output by the simulator 120, such as cross-sections and 2D radial grid views. In an illustrative embodiment, the évaluation module 118 can be used to evaluate the dynamic state of the réservoir during product and confirm or modify production forecasts and/or any uncertainties and risk factor associated therewith. The information provided by the évaluation module 118 can be used to update the réservoir development plan in the event that uncertainties or risks are unacceptable or new information is gathered. Changes or additions to equipment and operations of the réservoir development plan can be planned, and the modeling and simulation operations of the modules of the framework 100 can be repeated in an attempt to seek a more certain understanding of the planned production from the formation of interest over time.
When the réservoir development plan is complété, production from the réservoir may be carried out in accordance with a réservoir development plan. Production monitoring equipment can be used to gather information (e.g., historical field production pressures, pipelines pressures and flow rates, etc.). The réservoir development pian can be updated based upon such new information, and the réservoir simulator 120 can employ history matching where historical field production and pressures are compared to calculated values. The parameters of the réservoir simulator 120 may be adjusted until a reasonable match is achieved on a réservoir basis and usually for ail wells. In an exemplary embodiment, producing water cuts or water-oil ratios and gas-oil ratios are matched.
In an exemplary embodiment, the réservoir modeling software framework 100 of FIG. 2 may be embodied as software modules executing on a computer workstation as shown in FIG. 3. The software modules can be persistently stored in the hard disk drive(s) of the workstation and loaded into memory for execution by the CPU(s) of the workstation. One or more of the modules of the framework 100, such as the geological model 104, gridding modules 108A, 108B, fluid property model 112, and fluid property mapper module 116 can be integrated as a part of the framework 100 or altematively as plug-in module. A plug-in module may include software that adds spécifie capabllities to a larger host application (the framework 100), The host application may provide services which the plug-in can use, including, but not limited to, a way for plug-ins to register themselves with the host application and a protocol for the exchange of data with plug-ins. Plug-ins may dépend on the services provided by the host application and might not work by themselves. Conversely, the host application may operate independently of the plug-ins, making it possible for end-users to add and update plug-ins dynamicaily without needing to make changes to the host application.
In alternate embodiments, the réservoir modeling software framework 100 of FIG. 2 can be embodied in a distributed computing environment (such as a computing cluster or grid) or in a cloud computing environment.
FIGS. 4A - 4B depict an exemplary workflow for understanding a réservoir throughout the lifecycle of the réservoir in accordance with a first embodiment of the présent invention. The workflow begins in block 401 by defining a réservoir assessment plan for a formation of interest. The goal of the réservoir assessment plan may be to dérivé an understanding of the structure and stratigraphy of the formation of interest as well as a forecast of the hydrocarbons that are contained in the formation of interest. The réservoir assessment plan may dictate a number of data gathering operations and analyses, such as well drilling and logging, seismic analysis, laboratory core and fluid analysis, and downhole fluid analysis, as described above. In block 403, économie and risk analysis can be integrated into the réservoir assessment plan. Rîsk and uncertainty analysis may include representing uncertainties with probabilities based on a distribution of the expected values of the uncertain variables. Sensitivity analysis can also be used to address uncertain variables. Economies analysis may assign costs to the equipment and operations that make up the réservoir assessment plan. In block 405, the data gathering operations and analyses dictated by the réservoir assessment plan of block 401 may be carried out. In an exemplary embodiment, the résultant data of block 405 may be stored in the data store 102 of the réservoir modeling software framework 100 of FIG. 2.
In block 407, the résultant data of block 405 may be operated on by a géologie modeler (e.g., géologie modeler 104 of FIG. 2) to generate a three-dimensional geological model of the formation of interest. The three-dimensional geological model may include a framework that provides a description of the structure and stratigraphy of the formation of interest. In an exemplary embodiment, the geological model constructed in block 407 may provide a basic description of the formation of interest in terms of dimensions and unconformities (e.g., fractures, layers and permeability barriers). Details of an exemplary geological model 106 are described above with respect to the framework 100 of FIG. 2. Block 407 may also perform gridding and upsealing operation on the geological model as required. Details of exemplary gridding and upscaling operations are described above with respect to modules 108A and 108B of the framework 100 of FIG. 2. The operations of block 407 may dérivé a réservoir simulation grid 110 constructed from a grid of cells that are associated with petrophysical properties such as porosity, permeability, initial interstitial fluid saturation, and relative permeability and capillary pressure functions. For a fractured réservoir, a dual-porosity model and/or a dual-permeability model can be used. Local grid refinements (a finer grid embedded inside of a coarse grid) can also be used, for example to more accurately represent the near-wellbore multi-phase flow effects.
In block 409, the, résultant data of block 405 may be operated on by a fluid property modeler (e.g., fluid property modeler 112 of FIG. 2) to generale a fluid property model that characterizes the fluid properties of the formation of interest. The fluid property modeler employs a particular équation of state model, referred to herein as the FHZ EOS, that dérivés property gradients, pressure gradients and température gradients as a function of depth in the formation of interest. These gradients may be incorporated as part of the fluid property model. The property gradients derived from the FHZ EOS may include mass fractions, mole fractions, molecular weights, and spécifie gravities for a set of pseudocomponents of the formation fluid. Such pseudocomponents may include a heavy pseudocomponent representing asphaltenes in the formation fluid, a second distillate pseudocomponent that represents the non-asphaltene liquid fraction of the formation fluid, and a third light pseudocomponent that represents gases in the 20 formation fluid. The pseudocomponents derived from the FHZ EOS can also represent single carbon number (SON) components as well as other fractions or lumps of the formation fluid (such as a water fraction) as desired. The FHZ EOS can predict compositional gradients (including, but not limited to, an asphaltene concentration gradient) with depth thaï take into account lhe impacts of gravitational forces, chemical forces, thermal diffusion, etc., as described above. As part of block 409, a Flory-Huggins solubility model can be used in conjunction with compositional gradients produced by the FHZ EOS to dérivé a concentration profile of asphaltene pseudocomponents (e.g., asphaltene nanoaggregates and larger asphaltene clusters) and corresponding aggregate size of asphaltenes as a function of depth in the formation of interest as described above. The asphaltene concentration gradient can also be used to predict gradients for fluid properties (such as fluid density and fluid viscosity) that relate to asphaltene content. Details of an exemplary fluid property model 114 are described above with respect to the framework 100 of FIG. 2.
In block 411, the réservoir simulation grid derived in block 407 is initialized by mapping or interpolating the fluid properties of the formation fluids as represented by the fluid property model of block 411 to the grid cells of the réservoir simulation grid. Details of exemplary operations in carrying out such property transformations is described above with the respect to the module 116 of the framework 100 of FIG. 2.
in block 413, one or more users may review and analyze the information stored in the résultant réservoir simulation grid of block 411 in order to understand the structural properties and fluid properties of the formation of interest. For example, the évaluation module 118 of the framework 100 of FIG. 2 may provide for rendering of 3-D représentations of properties of the formation of înterest for use in full-field visualization. The évaluation module 118 can also display 2-D représentations of properties of the formation of înterest, such as cross-sections and 2-D radial grid views. In an illustrative embodiment, the évaluation module 118 can be used to characterize the réservoir (i.e., evaluate the static state of the réservoir before any production) and identify, confirm or modify reserves forecasts for the formation of interest and/or any uncertainties and risk factor associated therewith.
In an exemplary embodiment, the évaluation module 118 of framework 100 of FIG. 2 may render and display a 3-D représentation of the predicted fluid properties (such as gradients in predicted asphaltene concentration, predicted fluid density, predicted fluid viscosity, etc., which are based on the prédictions of the fluid property model of block 411), measured fluid properties (such as gradients in measured asphaltene concentration, measured fluid density, measured fluid viscosity, etc., which may be based on the data acquisition of block 405 and stored in the data store 102), and représentations of structural horizons and faults. The information displayed by the évaluation module 118 allows the user to evaluate the presence or absence of flow barriers in the formation. It can include other useful information such as other predicted property gradients, other measured property gradients, and measured geochemical fingerprints from réservoir fluid samples that characterize the réservoir fluids.
The user can view and navigate over the 3D représentation to assess réservoir compartmentalization (i.e., the presence or absence of flow barriers in the formation). More specifically, the presence of a flow barrier is indicated by discontinuities in the fluid properties (including, but not limited to, the asphaltene concentration gradient) of the réservoir simulation grid as well as discontinuities in the downhole fluid analysis measurements for corresponding well locations. Moreover, the presence of a flow barrier can be indicated by disagreement between measured asphaltene concentration and the predicted asphaltene concentration produced by the FHZ EOS modeling, even for those cases where there is no corresponding discontinuity in the fluid properties. The presence of a flow barrier is also indicated by a structural fault at corresponding locations. Such analysis can also be extended for assessment of flow barriers in a formation with multiple wells (i.e., multiwell analysis). In this scénario, if there is different compositional gradient between wells, this is an indication that there is a flow barrier (seal) between the wells or parts of the wells.
In block 415, the information derived by user review and analysis of the information stored in the résultant réservoir simulation grid in block 413 can be used to update (oroptimize) the réservoir assessment plan in the event that uncertainties or risks are unacceptable or new information is gathered. For example, additional data acquisition and testing can be added to the réservoir assessment plan that is intended to reduce the uncertainty as to flow barriers identifïed by the analysis of block 413.
In block 417, it may be determined whether the réservoir assessment plan is complété. If not, the workflow returns to block 405 to carry out such additional tests ΐη order to acquire additional data, and the modeling and simulation operations of the modules of the framework 100 can be repeated (blocks 407 to 415) ΐη an attempt to seek a more certain understanding of the formation of interest.
In the event that assessment is complété, the operations may continue to block 419 wherein a réservoir development plan may be defined. The réservoir development plan may define a strategy for producing hydrocarbons from the formation of interest, such as the number, location and trajectory of wells, the completion apparatus of wells, artificial lift mechanisms, enhanced recovery mechanisms (such as water flooding, steam injection for heavy oil, hydraulic fracturing for shale gas and the like), pipeline Systems, facilities, and the expected production of fluids (gas, oil, water) from the formation.
In block 421, économie and risk analysis can be integrated into the réservoir development plan. Risk and uncertainty analysis may include representing uncertainties with probabilities based on a distribution of the expected values of the uncertain variables. Sensitivity analysis can also be used to address uncertain variables. Economies analysis may assign costs to the equipment and operations that make up the réservoir development plan.
In block 423, details of the réservoir development plan may be input to a réservoir simulator (such as the simulator module 120 of the framework 100 of FIG. 2). The réservoir simulator may dérivé computational équations and associated time-varying data that represent the details of réservoir development plan over time. The computational équations derived by the réservoir simulator in block 423 may be based on the FHZ EOS that is employed by the fluid property modeling in step 409. As described above, the équations of the FHZ EOS can be extended to dérivé and simulate a variety of properties of the réservoir fluid, including, but not limited to:
PVT properties (e.g., phase envelope, pressure-temperature (PT) flash, constant composition expansion (CCE), différentiel libération (DL).
constant volume déplétion (CVD));
gas hydrate formation;
wax précipitation;
asphaltene précipitation; and scaling prédiction.
In block 425, the réservoir simulator may initialize the réservoir simulation grid with the rock properties and fluid properties stored in the réservoir simulation grid upon completion of réservoir characterization (or updated thereafter in block 415).
In block 427, the réservoir simulator may utilize the computational équations and associated time varying data representing the réservoir development plan as derived in block 423 together with the rock properties and fluid properties stored in the réservoir simulation grid initialized in block 425 to dérivé the pressure and fluid saturations (e.g., volume fractions) for each cell of the simulation grid as well as the production of each phase (i.e., gas, oil, water) over a number of time steps. In an exemplary embodiment, the réservoir simulator carries out frnite différence simulation as described above with respect to the réservoir simulator 120 of FIG. 2. The simulation can also be used to simulate a variety of properties of the réservoir fluid during réservoir development, such as predicting gas hydrate formation, wax précipitation, asphaltene précipitation, and scaling. These properties can be used to identify and evaluate flow assurance problems as well as possible remediation strategies.
In block 429, one or more users may review and analyze the properties of the formation of interest over time as output by the réservoir simulator ïn block 427. For example, the évaluation module 118 of the framework 100 of FIG. 2 may provide for construction of 3-D représentations of the properties of the formation of inlerest over time as output by the simulator for use in fullfield évaluation. The évaluation module 118 can also provide 2-D représentations of properties of the formation of interest over time as output by the simulator, such as cross-sections and 2-D radial grid views. In an illustrative embodiment, the évaluation module 118 can be used to evaluate the dynamic state of the réservoir during production and confirm or modify production forecasts and/or any uncertainties and risk factor associated therewith.
In block 431, the information derived by user review and analysis of the simulation results in block 429 can be used to update (or optimize) the réservoir development plan in the event that uncertainties or risks are unacceptable or new information is gathered.
In block 433, it may be determined whether the réservoir development plan is complété. If not, changes or additions to the equipment and operations of the réservoir development plan can be planned and the workflow returns to blocks 423 to repeat the modeling and simulation operations of blocks 423 to 431 in an attempt to seek a more certain understanding of the planned production from the formation of interest over time.
In the event that the réservoir development plan is complété, the operations may continue to block 435 wherein production may be carried out in accordance with the réservoir development plan. In block 437, production monitoring equipment can be used to gather information (e.g., historical field production pressures, pipelines pressures and flow rates, etc.).
In block 439, the réservoir development plan can be updated based upon the production information gathered in block 437 or other new information. If this occurs, the workflow can 10 return to blocks 423 to 431 for modeling and simulation of the réservoir. In this itération, the réservoir simulator can employ “history matching where historical field production and pressures may be compared to calculated values. The parameters of the réservoir simulator may be adjusted until a reasonable match is achieved on a réservoir basis and usually for ail wells. Producing water cuts or water-oil ratios and gas-oil ratios may be matched. These 15 operations can be repeated until production is complété (block 441) in order to optimize production decisions over the time of production of the réservoir.
FIGS. 5A - 5C depict a workflow for understanding a réservoir throughout the lifecycle of the réservoir in accordance with a second embodiment of the présent invention. The workflow begins in block 501 by defining a réservoir assessment plan for a formation of interest. The 20 goal of the réservoir assessment plan may be to dérivé an understanding of the structure and stratigraphy of the formation of interest as well as a forecast of the hydrocarbons that are contained in the formation of interest. The réservoir assessment plan may dictate a number of data gathering operations and analyses, such as well drilling and logging, seismic analysis, laboratory core and fluid analysis, and downhole fluid analysis that characterize rock properties 25 (e.g.. lithology, fractures, porostty, permeabllity, water saturation, oil saturation) and fluid properties (e.g., fluid density, fluid viscosity, composïtional components, GOR, formation volume factors, pressure, température, PH, color, others) as a function of location in the formation of interest as described above. Economie and risk analysis can be integrated into the réservoir assessment plan. Risk and uncertainty analysis may include representïng uncertainties with probabilités based on a distribution of the expected values of the uncertain variables. Sensitivity analysis can also be used to address uncertain variables. Economies analysis may assign costs to the equipment and operations that make up the réservoir assessment plan.
In block 503, rock property testing and analysis operations (e.g., well drilling and logging, seismic analysis, and laboratory core analysis) may be performed as dictated by the réservoir assessment plan of block 501, In block 505, the résultant rock property data of block 503 may be stored in the data store 102 of the réservoir modeling software framework 100 of FIG. 2.
In block 507, the résultant rock property data of block 503 may be loaded from the data store 102 and operated on by the géologie modeler 104 of FIG. 2 (e.g., Petrel Réservoir Modeling Software of Schlumberger Information Systems of Houston, Texas) to generate a threedimensional geological model of the formation of interest. The three-dimensional geological model is a framework that provides a description of the structure and stratigraphy of the formation of interest In an exemplary embodiment, the geological model constructed in block 507 may provide a basic description of the formation of interest in terms of dimensions and unconformities (e.g., fractures, layers and permeability barriers). Details of an exemplary geological model 106 are described above with respect to the framework 100 of FIG. 2. In block 509, the three-dimensional geological model derived in block 507 may be stored in the data store 102 of FIG. 2.
In block 511, gridding and upsealing operations may be performed on the geological model stored in block 509 as required. Details of exemplary gridding and upsealing operations are described above with respect to modules 108A and 108B of the framework 100 of FIG. 2. The operations of block 511 may dérivé a réservoir simulation grid constructed from a grid of cells that are associated with petrophysical properties such as porosity, permeability, initial interstïtial fluid saturation, and relative permeability and capillary pressure functions. For a fractured réservoir, a dual-porosity model and/or a dual-permeability model can be used. Local grid refinements (a finer grid embedded inside of a coarse grid) can also be used, for example to more accurately represent the near-wellbore multi-phase flow effects. In block 513, the threedimensional réservoir simulation grid derived in block 511 may be stored in the data store 102 of FIG. 2.
In block 515, fluid property testlng and analysis operations (e.g., laboratory core fluid analysis and downhole fluid analysis) may be performed as dictated by the réservoir assessment plan of block 501. In block 517, the résultant fluid property data of block 515 may be stored in the data store 102 of the réservoir modeling software framework 100 of FIG, 2.
In block 519, the résultant fluid property data of block 515 may be loaded from the data store 102 and may be operated on by the fluid property modeler 112 of FIG. 2 to generate a fluid property model that characterizes the fluid properties of the formation of interest. The fluid property modeler 112 may employ the FHZ EOS in order to dérivé property gradients, pressure gradients and température gradients as a function of depth in the formation of interest. These gradients may be incorporated as part of the fluid property model. The property gradients derived from the FHZ EOS may include mass fractions, mole fractions, molecular weights, and spécifie gravities for a set of pseudocomponents of the formation fluid. Such pseudocomponents may include a heavy pseudocomponent representing asphaltenes in the formation fluid, a second distillate pseudocomponent that represents the non-asphaltene liquid fraction of the formation fluid, and a third light pseudocomponent that présents gases in the formation fluid. The pseudocomponents derived from the FHZ EOS can also represent single carbon number (SCN) components as well as other fractions or lumps of the formation fluid (such as a water fraction) as desired. The FHZ EOS can predict compositional gradients (including, but not limited to, an asphaltene concentration gradient) with depth that takes into account the impacts of gravitational forces, chemîcal forces, thermal diffusion, etc. as described above. As part of block 519, a Flory-Huggins solubility model can be used in conjunction with compositional gradients produced by the FHZ EOS to dérivé a concentration profile of asphaltene pseudocomponents (e.g., asphaltene nanoaggregates and larger asphaltene clusters) and corresponding aggregate size of asphaltenes as a function of depth in the formation of interest as described above. The asphaltene concentration gradient can also be used to predict gradients for fluid properties (such as fluid density and fluid viscosity) that relate to asphaltene content. Details of an exemplary fluid property model 114 are described above with respect to the framework 100 of FIG. 2. In block 521, the fluid property model derived in block 519 is stored in the data store 102 of FIG. 2.
In block 523, downhole fluid analysis measurements of color of the formation fluids can be acquired at multiple locations in the formation, if not yet acquired as part of block 501. For example, the Quicksilver probe and InSitu fluid analyzer commercially available from Schlumberger can be used to perform such formation fluid color measurements.
In block 525, the formation fluid color measurements of block 523 may be converted to asphaltene concentration measurements, if not yet converted as part of the block 501. In an exemplary embodiment, this conversion may employ an empirical relation of the form:
ODdfa = C1*Wa + C2, where ODDfa is the measured color (i.e., optical density) of the formation fluid at a particular wavelength (this particular wavelength can vary over different réservoirs, but usually it will be in the ultra-violet or visible parts of the spectrum);
Wa is the corresponding mass fraction of asphaltenes; and
C1 and C2 are constants derived from empirical data, C1 being in the range of 0.1 - 30, and C2 close to 0.
In block 527, it may be determined if the asphaltene property gradient of the fluid property model as derived in block 519 is consistent with the asphaltene concentration measurements of block 525. In an exemplary embodiment, the consistency check of block 527 détermines whether the measured asphaltene concentration gradient is far from the predicted asphaltene concentration gradient. It can also involve comparisons between the other measured property gradients and corresponding predicted property gradients. If there are significant différences, it can be inferred that measurement errors hâve occurred (such as a tool failure) and the corresponding measurements may be disregarded (and possibly corrected if possible). This makes the analysis more robust. In an exemplary embodiment, the évaluation module 118 générâtes a graphïcal user interface screen that displays simultaneously the measured compositions, predicted properties, and measured properties, in order to allow a user to efficiently and effectively compare the measured and predicted properties to identify incorrect measurements.
In block 529, it may be determined if the fluid property model as derived in block 519 is consistent with the geological model of block 507 and/or the initial réservoir simulation grid of block 511. In an exemplary embodiment, the évaluation module 118 may generate a graphical user interface screen that displays the géologie model (in an exemplary embodiment, highlighting potential flow barriers) overlayed with the predicted fluid properties derived from the property modeler 112 as well as the measured fluid properties derived from downhole fluid analysis (or laboratory analysis). This interface allows the user to efficiently and effectively identify inconsistencies geological model of block 507 and/or the initial réservoir simulation grid of block 511. The graphical user interface can also depict other fluid properties (such as pressure) as a function of location in the réservoir in order to visually convey complimentary information regarding connectivity.
In the event that either one of the consistency checks of blocks 527 and 529 fails, the operations may continue to block 531 to résolve the inconsistency by analysis or additional testing and analysis. The fluid property model, the geological model and/or the initial réservoir simulation grid may be updated as appropriate in resolving the inconsistency as part of block 531. The predicted asphaltene property gradient of the fluid property model may be derived from the FHZ EOS as described above. The FHZ EOS may assume that the réservoir is connected and has achieved thermodynamic equilibrium (at least the asphaltenes hâve achieved thermodynamic equilibrium) over a range of depth of interest If the asphaltene concentration measurements of block 525 are consistent with the predicted asphaltene property gradient of the fluid property model, this resuit may suggest that the fluid property model accurately captures réservoir connectivity. If the asphaltene concentration measurements of block 525 are not consistent with the predicted asphaltene property gradient of the fluid property model, this resuit may suggest that the fluid property model does not accurately capture réservoir connectivity. In this case, the formation of interest may be studied further to check for non-equillbrium and/or sealing barriers, and the réservoir model may have to be redefined. It has been found that when the FHZ EOS does not accurately predict the asphaltene compositional gradient, there are likely previously unidentified barriers compartmentalizing the réservoir.
In the event that the consistency checks of blocks 527 and 529 pass (or the inconsistencies are resolved in block 531), the operations continue to block 533 wherein the réservoir simulation grid stored in block 513 (or updated in block 531) may be initialized by mapping or interpolating the fluid properties ofthe formation fluids as represented by the fluid property model of block 519 (or updated as part of block 531 ) to the grid cells of the réservoir simulation grid. Details of exemplary operations in carrying out such property transformations is described above with the respect to the module 116 of the framework 100 of FIG. 2.
In block 535, one or more users may review and analyze the information stored in the résultant réservoir simulation grid of block 411 in order to understand the structural properties and fluid properties of the formation of interest. For example, the évaluation module 118 of the framework 100 of FIG. 2 may provide for rendering of 3-D représentations of properties of the formation of interest for use in full-fîeld visualization. For example, such visualization can depict pressures and fluid saturations as well as compositions of each fluid phase over the grid cells of the simulation grid. The évaluation module 118 can also display 2-D représentations of properties of the formation of interest, such as cross-sections and 2-D radial grid views. In the illustrative embodiment, the évaluation module 118 can be used to characterize the réservoir (i.e., evaluate the static state of the réservoir before any production) and identify, confirm or modify reserves forecasts for the formation of interest and/or any uncertainties and risk factor associated therewith. As part of block 535, the information derived by user review and analysis of the information stored in the résultant réservoir simulation grid in block 413 can be used to update (or optimize) the réservoir assessment plan in the event that uncertainties or risks are unacceplable or new information is gathered.
In an exemplary embodiment, the évaluation module 118 of framework 100 of FIG. 2 may render and display a 3-D représentation of the predicted fluid properties (such as gradients in predicted asphaltene concentration, predicted fluid density, predicted fluid viscosity, etc., which are based on the prédictions of the fluid property modeling), measured fluid properties (such as gradients in measured asphaltene concentration, measured fluid density, measured fluid viscosity, etc., which are based on the réservoir fluid analysis and stored in the data store 102), and représentations of structural horizons and faults. The information displayed by the évaluation module 118 may allow a user to evaluate the presence or absence of flow barriers in the formation. It can include other useful Information, such as other predicted property gradients, other measured property gradients, and measured geochemical fingerprints from réservoir fluid samples that characterize the réservoir fluid. The user can view and navigate over the 3D représentation to assess réservoir compartmentalization (i.e., the presence or absence of flow barriers in formation). More specifically, the presence of a flow barrier is indicated by discontinuities in the fluid properties (including, but not limited to, the asphaltene concentration gradient) of the réservoir simulation grid as well as discontinuities in the downhole fluid analysis measurements for corresponding well locations. Moreover, the presence of a flow barrier can be indicated by disagreement between measured asphaltene concentration and the predicted asphaltene concentration produced by the FHZ EOS modeling, even for those cases where there is no corresponding discontinuity in the fluid properties. The presence of a flow barrier may also be indicated by a structural fault at corresponding locations. Such analysis can also be extended for assessment of flow barriers in a formation with multiple wells (i.e., multiwell analysis). In this scénario, if there is different compositional or property gradient between wells, this may be an indication that there is a flow barrier (seal) between the wells or parts of the wells.
In block 537, it may be determined whether the réservoir assessment plan is complété. If not, changes or additions to the tests and analyses of the assessment plan can be planned and the workflow returns to block 510 to carry out such additional tests in order to acquire additional data, and the modeling and simulation operations of the modules of the framework 100 can be repeated (blocks 503 to 535) in an attempt to seek a more certain understanding of the formation of interest.
In the event that assessment is complété, the operations may continue to block 539 wherein a réservoir development plan may be defined. The réservoir development plan defines a strategy for producing hydrocarbons from the formation of interest, such as the number, location and trajectory of wells. the completion apparatus of wells, artificial lift mechanisms, enhanced recovery mechanisms (such as water flooding, steam injection for heavy oil, hydraulic fracturing for shale gas and the like), pipeline Systems, facilities, and the expected production of fluids (gas, oil, water) from the formation. As part of block 539, économie and risk analysis can be integrated into the réservoir development plan. Risk and uncertainty analysis may involve representing uncertainties with probabilities based on a distribution of the expected values of the uncertain variables. Sensitivity analysis can also be used to address uncertain variables. Economies analysis may assign costs to the equipment and operations that make up the réservoir development plan. As part of part of block 539, computational équations and associated time-varying data that represent the details of réservoir development plan over time may be inpul (or derived by) the réservoir simulator 120 of FIG. 2. The computational équations derived by the réservoir simulator in block 539 may be based on the FHZ EOS that is employed by the fluid property modeling in step 519. As described above, the équations ofthe FHZ EOS can be extended to dérivé and simulate a variety of properties of the réservoirfluid, including, but not limited to:
PVT properties (e.g., phase envelope, pressure-temperature (PT) flash, constant composition expansion (CCE), différentiel libération (DL), constant volume déplétion (CVD));
gas hydrate formation;
wax précipitation;
asphaltene précipitation; and sealing prédiction.
In block 541, the réservoir simulator 120 may initialize the réservoir simulation grid with the rock properties and fluid properties stored in the réservoir simulation grid upon completion of réservoir characterization of block 535 (or updated thereafter).
In block 543, the réservoir simulator 120 may utilize the computational équations and associated time varying data representing the réservoir development plan as derived in block 539 together with the rock properties and fluid properties stored in the réservoir simulation grid initialized in block 541 to dérivé the pressure and fluid saturations (e.g., volume fractions) for each cell of the simulation grid as well as the production of each phase (i.e., gas, oil, water) over a number of time steps. In an exemplary embodiment, the réservoir simulator 120 may carry out finite différence simulation as described above. The simulation can also be used to simulate a variety of properties of the réservoir fluid during réservoir development, such as predicting gas hydrate formation, wax précipitation, asphaltene précipitation, and sealing. These properties can be used to identify and evaluate flow assurance problème as well as possible remediation strategies.
In block 545, one or more users may review and analyze the properties of the formation of interest over time as output by the réservoir simulator in block 543. For example, the évaluation module 118 of the framework 100 of FIG. 2 may provide for rendering of 3-D représentations of the properties of the formation of interest over time as output by the simulator 120 for use in fullfield visualization. The évaluation module 118 can also display 2-D représentations of properties of the formation of interest over time as output by the simulator 120, such as crosssections and 2-D radial grid views. In the illustrative embodiment, the évaluation module 118 can be used to evaluate the dynamic state of the réservoir during production and confirm or modify production forecasts and/or any uncertainties and risk factor associated therewith.
In block 547, the information derived by user review and analysis of the simulation results in block 429 can be used to update (or optimize) the réservoir development plan in the event that uncertainties or risks are unacceptable or new information is gathered.
In block 549, it may be determined whether the réservoir development plan is complété. If not, changes or additions to the equipment and operations of the réservoir development plan can be planned and the workflow may return to block 539 to repeat the modeling and simulation operations of blocks 539 to 547 in an attempt to seek a more certain understanding ofthe planned production from the formation of interest over time.
In the event that the réservoir development plan is complété, the operations may continue to block 551 wherein production may be carried out in accordance with the réservoir development plan. In block 553, production monitoring equipment can be used to gather information (e.g., hislorical field production pressures, pipelines pressures and flow rates, etc.).
In block 555, the réservoir development plan can be updated based upon the production information gathered in block 553 or other new information. If this occurs, the workflow can return to blocks 539 to 547 for modeling and simulation of the réservoir. In this itération, the réservoir simulator can employ history matching where hislorical field production and pressures may be compared to calculated values. The parameters of the réservoir simulator may be adjusted until a reasonable match is achieved on a réservoir basis and usually for ail wells. In an embodiment, producing water cuts or water-oil ratios and gas-oil ratios may be matched. These operations can be repeated until production is complété (block 557) in order to optimize production decisions over the time of production of the réservoir.
Advantageously, the présent invention may automate the application of the FHZ EOS model calculations to the réservoir modeling and simulation framework, which may allow réservoir compartmentalization (the presence or absence of flow barrier in the réservoir) to be assessed more quickly and easily. Additionally, automated intégration of FHZ EOS model calculations into the réservoir modeling and simulation framework may allow the compositional gradients produced by the FHZ EOS model calculations (particularly asphaltene concentration gradients) to be combined with other data, such as géologie and other petrophysical data, which may allow for more accurate assessment of réservoir compartmentalization.
There hâve been described and illustrated herein several embodiments of a method and system for modeling, evaluating and simulating hydrocarbon bearing subterranean formations. While particular embodiments of the invention hâve been described, it is not intended that the 5 invention be limited thereto, as it is intended that the invention be as broad in scope as the art will allow and that the spécification be read likewîse. Thus, while particular data acquisition méthodologies and tools hâve been disclosed, it will be appreciated that other data acquisition méthodologies and tools may be within the scope of the present disclosure as well. In addition, while particular types of geological models, fluid property models and réservoir simulation models hâve been disclosed, it will be understood that similar models can be used. Moreover, while particular configurations of the modeling framework hâve been disclosed, it will be appreciated that other configurations could be used as well. It will therefore be appreciated by those skilled in the art that yet other modifications could be made to the provided invention without deviating from its spirit and scope as claimed.

Claims (23)

  1. WHAT IS CLAIMED IS:
    1. A method for evaluating a subterranean formation, the method comprising:
    (a) deriving a first model of the formation, the first model representing rock properties as a
    5 function of location in the formation;
    (b) deriving a second model of the formation, the second model representing fluid properties as a function of location in the formation, wherein the fluid properties of the second model characterize asphaltene concentration as a function of location in the formation; and (c) deriving a third model of the formation, the third model including rock properties as a
    10 function of location in the formation that are based on the rock properties of the first model, and the third model also including fluid properties as a function of location in the formation that are based on the fluid properties of the second model, wherein the fluid properties of the third model characterize asphaltene concentration as a function of location in the formation.
  2. 2. A method according to claim 1, wherein:
    15 the first model has a first grid system, and the third model has a second grid system that is coarser than said first grid system.
  3. 3. A method according to claim 1, wherein:
    the second model represents a continuous change in certain fluid properties as a function of location in the formation.
    20
  4. 4. A method according to claim 1, further comprising:
    characterizing asphaltene concentration as a function of location in the formation;
    comparing the asphaltene concentration as a function of location in the formation résultant from said characterizing to corresponding prédictions of asphaltene concentration in the formation as represented by the second model; and
    25 selectively integrating the fluid properties of the second model into the third model based upon the results of said comparing.
  5. 5. A method according to claim 4, wherein:
    the fluid properties of the second model are integrated into the third model only if the asphaltene concentration as a function of location in the formation résultant from said
    30 characterizing is consistent with corresponding prédictions of asphaltene concentration in the formation as represented by the second model.
  6. 6. A method according to claim 4, wherein:
    asphaltene concentration as a function of location in the formation is characterized from fluid analysis selected from a group consisting of: ï) downhole fluid color measurements performed within a wellbore traversing the formation, and ii) laboratory fluid color measurements.
  7. 7. A method according to claim 6, wherein:
    the downhole fluid color measurements are converted to asphaltene concentration measurements employing an empirical relation of the form:
    ODdfa = C1*Wb + C2, where OD0FA is the measured color of formation fluid at a particular wavelength;
    Wa îs the corresponding mass fraction of asphaltenes; and
    C1 and C2 are constants.
  8. 8. A method according to claim 1, wherein:
    the fluid properties of the second model are selectively integrated into the third model based upon a détermination of consistency between the fluid properties of the second model and the rock properties of one of the second model and third model.
  9. 9. A method according to claim 1, further comprising:
    visualizing the properties of the third model to evaluate the presence or absence of flow barriers in the formation.
  10. 10. A method according to claim 9, further comprising:
    the visualizing displays information that describes the formation in order to evaluate the presence or absence of flow barriers in the formation, wherein said information is selected from a group consisting of: predicted asphaltene concentration gradients defined by the third model, measured asphaltene concentration gradients, structural faults defined by the third model, predicted fluid density gradient defined by the third model, measured fluid density gradient, predicted fluid viscosity gradient defined by the third model, measured fluid viscosity gradient, other predicted property gradients defined by the third model, other measured property gradients, and measured geochemical fingerprints.
  11. 11. A method according to claim 9, wherein:
    the visualizing is performed for multiple wellbores that traverse the formation in order to evaluate the presence or absence of flow barriers between the multiple wellbores.
  12. 12. A method according to claim 9, wherein:
    the visualizing is used to update a réservoir assessment plan for the formation.
  13. 13. A method according to claim 12, wherein:
    the réservoir assessment plan is updated to define data acquisition operations that are intended to reduce uncertainty with respect to the presence or absence of flow barriers in the formation.
  14. 14. A method according to claim 1, wherein:
    the third model is used to simulate production of fluids from the formation over time.
  15. 15. A method according to claim 1, wherein:
    the second model is derived by solvîng an équation of state model that characterizes asphaltene concentration as a function of location in the formation.
  16. 16. A method according to claim 15, wherein:
    the équation of state model dérivés property gradients, pressure gradients and température gradients as a function of depth in the formation.
  17. 17. A method according to claim 16, wherein:
    the property gradients derived from the équation of state model comprise one or more of mass fractions, mole fractions, molecular weights, and spécifie gravities for a set of pseudocomponents of the formation fluid.
  18. 18. A method according to claim 17, wherein:
    said set of pseudocomponents include a heavy pseudocomponent representing asphaltenes in the formation fluid, a second distillate pseudocomponent that represents the nonasphaltene liquid fraction of the formation fluid, and a third light pseudocomponent that represents gases in the formation fluid.
  19. 19. A method according to claim 17, wherein:
    said set of pseudocomponents represents single carbon number (SON) components as well as other fractions of the formation fluid.
  20. 20. A method according to claim 15, wherein;
    the équation of state model predicts compositional gradients with depth that take into account the impacts of at least one factor selected from the group consisting of gravitational forces, chemical forces, and thermal diffusion.
  21. 21. A method according to claim 20, wherein:
    the characterization of asphaltene concentration as a function of depth as derived from the équation of state model is used to generate a concentration profile of asphaltene pseudocomponents and corresponding aggregate size of asphaltenes as a function of location in the formation.
  22. 22. A method according to claim 21, wherein:
    the asphaltene pseudocomponents comprise asphaltene nanoaggregates and larger asphaltene clusters.
  23. 23. A method according to claim 15, wherein:
    5 the characterization of asphaltene concentration as a function of depth as derived from the équation of state model is used to predict gradients for at least one particular fluid property that relates to asphaltene concentration, the particular fluid property selected from the group consisting of fluid density and fluid viscosity.
OA1201300376 2011-03-09 2011-12-14 Method and systems for reservoir modeling, evaluation and simulation. OA16556A (en)

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