WO2017165949A1 - Procédés, systèmes et dispositifs permettant de modéliser les propriétés d'un réservoir - Google Patents

Procédés, systèmes et dispositifs permettant de modéliser les propriétés d'un réservoir Download PDF

Info

Publication number
WO2017165949A1
WO2017165949A1 PCT/CA2016/050369 CA2016050369W WO2017165949A1 WO 2017165949 A1 WO2017165949 A1 WO 2017165949A1 CA 2016050369 W CA2016050369 W CA 2016050369W WO 2017165949 A1 WO2017165949 A1 WO 2017165949A1
Authority
WO
WIPO (PCT)
Prior art keywords
production
values
subterranean formation
model
models
Prior art date
Application number
PCT/CA2016/050369
Other languages
English (en)
Inventor
Stefan ZANON
David CAMPAGNA
Graham Andrew Robert NOBLE
Original Assignee
Nexen Energy Ulc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Nexen Energy Ulc filed Critical Nexen Energy Ulc
Priority to US16/088,949 priority Critical patent/US20190120022A1/en
Priority to CA3019180A priority patent/CA3019180A1/fr
Priority to PCT/CA2016/050369 priority patent/WO2017165949A1/fr
Publication of WO2017165949A1 publication Critical patent/WO2017165949A1/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B43/00—Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells
    • E21B43/25—Methods for stimulating production
    • E21B43/26—Methods for stimulating production by forming crevices or fractures
    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells

Definitions

  • the present application relates to the field of reservoir modelling, and particularly to methods, systems and devices for modelling unconventional oil reservoir production based on collected physical data.
  • Hydrocarbon exploration involves trade-offs between the number and spacing of wells (and associated costs) and the geological and commercial risk based on available data which may impact production forecasting and resource development planning.
  • Type curves can be used to estimate reservoir production for new wells by averaging existing wells. However, in some instances, type curves may be difficult to adjust for differences, and may not account for subtle changes across an area or over time.
  • Simulations can also be used to model reservoir properties; however, simulations may require large amounts of input data which may be expensive to obtain, and are expensive from both a time and computational resource perspective.
  • a method of modelling hydrocarbon production rates for a subterranean formation includes: obtaining, by at least one processor, production data for at least one well in the subterranean formation; based at least in part on geological data for the subterranean formation, identify, at the at least one processor, a range of potential values for each of a plurality of parameters, the plurality of parameters including at least one parameter representative of geological characteristics of the subterranean formation, and fracture parameters; where each set of values including a selection from each of the ranges for the plurality of parameters defining a potential subterranean formation model, and where sets of values including different combinations of values for the plurality of parameters define a set of potential subterranean formation models; matching at least a portion of the set of potential subterranean formation models to the production data for the at least one well by iteratively: inputting, by the at least one processor, a set of parameter values selected from the ranges of potential
  • a system for modelling hydrocarbon production rates for a subterranean formation includes at least one processor configured for: obtaining production data for at least one well in the subterranean formation; based at least in part on geological data for the subterranean formation, identify a range of potential values for each of a plurality of parameters, the plurality of parameters including at least one parameter representative of geological characteristics of the subterranean formation, and fracture parameters; where each set of values including a selection from each of the ranges for the plurality of parameters defining a potential subterranean formation model, and where sets of values including different combinations of values for the plurality of parameters define a set of potential subterranean formation models; matching at least a portion of the set of potential subterranean formation models to the production data for the at least one well by iteratively: inputting a set of parameter values selected from the ranges of potential values to an analytical fracture model to generate a production model for the subterran
  • a computer-readable medium or media having stored thereon computer-readable instructions which when executed by at least one processor configured the at least one processor for: obtaining, by the at least one processor, production data for at least one well in the subterranean formation; based at least in part on geological data for the subterranean formation, identify, at the at least one processor, a range of potential values for each of a plurality of parameters, the plurality of parameters including at least one parameter representative of geological characteristics of the subterranean formation, and fracture parameters; where each set of values including a selection from each of the ranges for the plurality of parameters defining a potential subterranean formation model, and where sets of values including different combinations of values for the plurality of parameters define a set of potential subterranean formation models; matching at least a portion of the set of potential subterranean formation models to the production data for the at least one well by iteratively: inputting, by the at least one processor, a set
  • Fig. 1 is a cross sectional view of an example geological formation and well;
  • Fig. 2 is an example system to which aspects of the present disclosure may be applied;
  • FIG. 3 is a perspective sectional view of an example horizontal well portion with fractures;
  • Figs. 4 is a top view of an example horizontal well portion with fractures;
  • FIGs. 5, 6 and 7 are flowcharts illustrating aspects of example methods for modelling reservoir properties
  • Figs. 8 and 9 is are stimulated area vs. error value plots
  • Fig. 10 shows an example geographic map showing different production forecasts for different portions of a resource
  • Fig. 11 shows a graph showing examples of different probabilistic forecasts of production rates.
  • aspects of the present disclosure may provide analytic devices, systems and methods for modelling resource production which are computationally less intensive or less time consuming than a complex simulation. In some embodiments, aspects of the present disclosure may have a higher degree of confidence in their models than a type curve or other similar model. [0021] In some embodiments, aspects of the present disclosure may provide devices, systems and methods for modelling resource production based on incomplete or less information than would be necessary for other processes.
  • aspects of the disclosure may, in some instances, provide a practical method for spatially modelling value to target areas of highest potential investment return.
  • Fig. 1 illustrates a cross-sectional view of a subterranean resource or geological formation 1 10 which may include a number of different layers of materials having different physical characteristics as illustrated in Fig. 1 by the lines in the formation. It should be understood that these lines are illustrative only and that geological formations may have any number of layers or types of material which may not have distinct delineations but may be gradual or may contain mixtures or combinations of different material. There may also be lateral and/or vertical variations in the types of material contained within any of the geological formations. [0024] In evaluating the subsurface or subterranean formations, in some examples, data is collected from one or more wells 100 drilled into or around the formations.
  • the wells are exploratory wells, production wells or wells for any other purpose.
  • the wells may include vertical wells 100, horizontal wells 105, or any wells of any direction or structure, and/or any combination thereof.
  • data collected from the well(s) 100 can include or can be used to create logs of the geologic formations penetrated by the well(s). The data can be collected from core samples or by measurements taken by devices in the borehole.
  • the well data collected or generated from well measurements can include, but are not limited to, gamma ray logs, bulk density logs, neutron density logs, induction resistivity logs, and/or well core or image data.
  • the geological formation may include hydrocarbon-bearing layers having low permeability such as shale or tight sandstone. Such formations may be suitable for hydrocarbon extraction using hydraulic fracturing technologies. In some instances, such unconventional plays may have large spatial variability, may involve multiple hydrocarbon fluids phases, and/or may have uncertainties in fracturing areas and permeability. [0028] As such, in some instances, traditional techniques for conventional oil well drilling may be unsuitable for these unconventional resources.
  • the embodiments of the devices, systems and methods described herein may be implemented in a combination of both hardware and software. These embodiments may be implemented on programmable computers, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements or a combination thereof), and at least one communication interface.
  • Program code may be applied to input data to perform the functions described herein and to generate output information.
  • the output information may be applied to one or more output devices.
  • the communication interface may be a network communication interface.
  • the communication interface may be a software communication interface, such as those for inter-process communication.
  • there may be a combination of communication interfaces implemented as hardware, software, and combination thereof.
  • devices having at least one processor may be configured to execute software instructions stored on a computer readable tangible, non-transitory medium.
  • each embodiment represents a single combination of inventive elements, other examples may include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, other remaining combinations of A, B, C, or D, may also be used.
  • the technical solution of embodiments may be in the form of a software product.
  • the software product may be stored in a non-volatile or non-transitory storage medium, which can be a compact disk read-only memory (CD-ROM), a USB flash disk, or a removable hard disk.
  • the software product includes a number of instructions that enable a computer device (personal computer, server, or network device) to execute the methods provided by the embodiments.
  • the embodiments described herein are implemented by physical computer hardware, including computing devices, servers, receivers, transmitters, processors, memory, displays, and networks.
  • the embodiments described herein provide useful physical machines and particularly configured computer hardware arrangements.
  • the embodiments described herein are directed to electronic machines and methods implemented by electronic machines adapted for processing and transforming electromagnetic signals which represent various types of information.
  • the embodiments described herein pervasively and integrally relate to machines, and their uses; and the embodiments described herein have no meaning or practical applicability outside their use with computer hardware, machines, and various hardware components. Substituting the physical hardware particularly configured to implement various acts for non-physical hardware, using mental steps for example, may substantially affect the way the embodiments work.
  • Fig. 2 shows an example system 200 include one or more devices 205 which may be used to model or predict hydrocarbon production rates.
  • a device 205 may be a computational device such as a computer, server, tablet or mobile device, or other system, device or any combination thereof suitable for accomplishing the purposes described herein.
  • the device 205 can include one or more processor(s) 210, memories 215, and/or one or more devices/interfaces 220 necessary or desirable for input/output, communications, control and the like.
  • the processor(s) 210 and/or other components of the device(s) 205 or system 250 may be configured to perform one or more aspects of the processes described herein.
  • the device(s) 205 may be configured to receive or access data from one or more volatile or non-volatile memories 215, or external storage devices 225 directly coupled to a device 205 or accessible via one or more wired and/or wireless network(s) 260.
  • external storage device(s) 225 can be a network storage device or may be part of or connected to a server or other device.
  • the data may be accessed from one or more public databases.
  • Such data can, in some examples, include less well characteristic and / or production data than may be available for well data from an internal source.
  • well production data may be provided as an aggregate of multiple wells or for an area without any information as to the number or size of fractures.
  • well production data may be provided over time in less granular time periods.
  • the methods, devices, and systems described herein may generate models which account for such unknown(s).
  • the device(s) 205 may be configured to receive or access data from sensors or devices 230 in the field. These sensors or devices 230 may be configured for collecting or measuring well, seismic or other geological and/or physical data. In some examples, the sensor(s)/device(s) 230 can be configured to communicate the collected data to the device(s) 205 and/or storage device(s) 225 via one or more networks 260 or otherwise. In some examples, the sensors or devices 230 may be connected to a local computing device 240 which may be configured to receive the data from the sensors/devices 230 for local storage and/or communication to the device(s) 205 and/or storage device(s) 225.
  • a client device 250 may connect to or otherwise communicate with the device(s) 205 to gain access to the data and/or to instruct or request that the device(s) 205 perform some or all of the aspects described herein.
  • a well may have multiple hydraulic fractures 310, which may be transverse or in any other orientation with respect to the well.
  • the wellbore 120 in Fig. 3 is horizontal; however, in other examples, fractures can be made relative to a wellbore of any orientation.
  • Fig. 4 shows a top-down view of the horizontal well 120 including four fractures 310.
  • a well with multiple fractures can be modelled as equally- spaced bi-wing transverse fractures.
  • the model can assume that there is a no flow boundary 315 at a midpoint between each fracture.
  • Fig. 4 shows an example drainage area for a single fracture 310.
  • fracture parameters can include a number of fractures, a fracture drainage area half-length Y e , a fracture drainage area half width X e , a fracture half-length X f , and the like.
  • the system can be configured to treat each fracture of a multi- fracture well as multiple wells with a single fracture each. In some instances, this may simplify the models and may reduce the computational load on the system.
  • the model can account for spatial differences in geology through geostatistical realization. Multiple models are run on a finite and discrete number of areas with a larger defined region. Distributions can then be calculated for each area. The model can represent one region over a play or can be run on several.
  • FIG. 5 shows a flowchart illustrating aspects of an example method 500 for modelling hydrocarbon production rates for a subterranean formation.
  • one or more processor(s) 210 and/or other aspects of device(s) 205 may be configured to receive, access, compile or otherwise obtain production data for at least one well in the subterranean formation 110.
  • the production data can be obtained from one or more memories 215, storage devices 215, 225, and/or sensors or field devices 230, 240.
  • the production data can include periodic (e.g. daily, weekly, monthly, etc.) production values, cumulative production values or any other data values from which suitable production data can be calculated.
  • the production data for the well(s) should span at least 6 months of normalized production in order to produce meaningful results.
  • the processor(s) can also obtain drilling data, completion data, and/or data associated with geological properties.
  • data associated with geological properties can include well logs such as gamma ray well log(s), bulk density well log(s), neutron density well log(s), resistivity well log(s), core and/or well image data, nuclear magnetic resonance log(s), and/ or any other well log that can be measured in the well.
  • well logs such as gamma ray well log(s), bulk density well log(s), neutron density well log(s), resistivity well log(s), core and/or well image data, nuclear magnetic resonance log(s), and/ or any other well log that can be measured in the well.
  • the processor(s) drilling and/or completion data can include fracture heights, fracture half lengths, a number of fractures, a fracture drainage area half- lengths, a fracture drainage area half widths, a fracture half-lengths, well lengths, and the like.
  • data may be obtained from internal data sources, or data collected from drilled wells and fracturing processes.
  • data may be obtained from public sources such as a data retrieved from a government or otherwise public fracturing database.
  • This public data may include production data for which production values for multiple fractures and/or wells may be combined into a single value.
  • the public data may not include all fracture characteristics or geological data.
  • the methods, systems and devices herein may accommodate for incomplete data sources such as public data or limited confidential data from a third party while still providing a production model with a reasonable degree of confidence.
  • the processor(s) may combine internal and external data sources.
  • the processor(s) identify a range of values for each parameter in a set of parameters for the subterranean formation.
  • the range of values for a parameter can be identified by determining a possible range of values based on geological data.
  • the processors can receive or otherwise obtain ranges of values from one or more input sources which may be based at least in part on the collected geological data for the subterranean formation.
  • the set of parameters can include one or more geological parameters representative of geological characteristics of the subterranean formation.
  • geological parameters can include one or more of formation height, reservoir depth, porosity, permeability, water saturation, and the like.
  • the geological parameters can include pressure properties, and/or rock and fluid properties including, for example, pressure gradient, initial pressure, operating conditions such as early or late well flowing pressure, months to well flowing pressure change, temperature gradient, reservoir temperature, rock compressibility, water compressibility, API (American Petroleum Institute) gravity, gas-to-oil ratio, combined gas gravity, relative gas permeability, residual gas saturation, critical gas saturation, gas viscosity, condensate gas ratio and the like.
  • API American Petroleum Institute
  • the set of parameters can include well parameters such as completion parameters and fracture parameters. These parameters can include fracture heights, fracture half lengths, a number of fractures, fracture drainage area half-lengths, fracture drainage area half widths, fracture half-lengths, well lengths, and the like.
  • one or more parameters may be fixed or constant based on the parameter itself or the obtained data. These parameter(s) will have a single value. However, other parameters may be unknown or may not be pinpointed exactly, so the processor(s) generate or receive a range of potential values for the parameter(s). These ranges can be based on the obtained geological and/or other obtained data.
  • the processor(s) can generate or receive parameter ranges which would be reasonable or possible based on log data, geostatistical models and/or extrapolated data between wells.
  • the processor(s) can generate or receive parameter ranges based on correlations with other parameters.
  • pressure(s) may be correlation with reservoir depth.
  • the range of values may be based on correlations with other geological formations having similar geological data.
  • the processor(s) can generate or receive a granularity or increment by which the ranges of parameter values can be varied in the system.
  • Each combination of values including a selection from each range for the set of parameters corresponds to, defines or otherwise represents a potential subterranean formation model, and the total set of different combinations of values for the set of parameters defines a set of potential subterranean formation models.
  • the processor(s) match the set of potential subterranean formation models to the production data for the well(s).
  • the matching is based on an analytical fracture model.
  • the analytical fracture model can generate a production model based on the geological parameters.
  • the base analytical model can include a number of unknowns which cannot be solved directly. In some embodiments, a number of these unknowns can be combined into a single stimulated area value. In some examples, the stimulated area value can be based on a number of parameters in the set of parameters defining or otherwise associated with properties of a subterranean formation model.
  • the processor(s) can generate a production model for the potential subterranean formation model corresponding to the set of input values. In some examples, the processor(s) can determine a stimulated area value for the generated production model.
  • the processor(s) compare the generated production model with the production data from the well(s) to generate an error value between the production of the generated model and the physical well production data.
  • the error value may be a total error over the production period, an average error, a maximum error, or any other error metric.
  • the error value may be based on individual production values for each time period.
  • the error value may be based on one or more of cumulative gas production, gas production rate, cumulative oil production, oil production rate, and the like.
  • the processor(s) are configured to select parameter values for inputting in a subsequent iteration based on a machine learning algorithm and previous error values.
  • the processor(s) can be configured to use a genetic algorithm to select subsequent parameter values for generating subsequent models.
  • the genetic algorithm can optimize or improve the searching of a large solution space. It can include selecting several sets of random values from all parameters and solving for the objective matching function. The results which provide the best matching function result can be merged together to find new sets of values for testing. This process is iterated with the best matching function results from each iteration used in subsequent sets of values for testing.
  • random variations can be introduced to input sets. In some instances, this can reduce the chance of solutions converging on a local optimal rather than a global optimal.
  • the processor(s) identify the models from the set of potential subterranean formation models which fit the production profile from the production data within a defined error threshold. In some examples, this includes identifying the models having an error value less than the defined error threshold.
  • the error threshold may be a statically defined threshold. In other examples, the error threshold may be based on a defined percentile of the models.
  • the error at different stages of production can be weighted differently. For example, if early production results are more important, the error for earlier stages of production can be weighted more than error for later stages of production; and vice versa. These weightings can be applied to objective matching functions.
  • the determination of the stimulated area values includes performing two runs to deliver a stable region. On the first run, a consistent number (e.g. 5000) of trials are performed on each run. On the second run, the model can be rerun with the last good solution as a starting point to ensure tight clustering of values. Again a consistent number of runs should be used. In some embodiments, three criteria are then used to define an acceptable solution space. First, a minimum absolute error of at least 20% should be achieved for a run to be considered valid. All runs should be completed with similar weighting to ensure the results are comparable. Second, the mean error can be examined as it progresses through the solution space. Cases that are within a best fit mean error will be retained.
  • a consistent number e.g. 5000
  • the maximum error of 2x the minimum error should be used to bound the cases.
  • the corresponding total error for this stable region of running average stimulated area value can be used to pick a minimum and maximum stimulated area value .
  • different mechanisms for determining the stimulated area values may be used.
  • the processor(s) select a range of stimulated area values from the identified models which fit the production profile from the production data within the defined error threshold. In some examples, the selected range is based on the models having the lowest error values. In some examples, the selected range is based on a concentration of stimulated area values which correspond to models having a low error value.
  • the processor(s) create a forecast production model for the subterranean resource having inputs based on the geological data, and stimulated value input(s) based on the selected range.
  • the process for forecasting production model is described in the following sections.
  • the matching process at 530 is based on an analytical fracture model.
  • the analytical fracture model can involve a gas model, an oil model or both.
  • Fig. 6 shows an example flowchart 600 outlining aspects of an example gas model.
  • the processor(s) generate or receive physical input values or ranges of physical input values based on obtained geological data.
  • geological data can be derived from petrophysical interpretation of well logs from adjacent or nearby wells.
  • the geological data can include time steps, pressure differences (initial, early, late), net pay, gas permeability, porosity, drainage area half width, fracture spacing half distance, condensate gas ratio, and the like.
  • the processor(s) may calculate other inputs which may be dependent or otherwise correlated with the physical inputs or other calculated inputs. These calculated inputs or correlations may include gas z factor calculations, critical temperatures and pressures for miscellaneous gasses, gas viscosity correlations, gas compressibility correlations, gas pseudopressure miscellaneous gasses, temperature, skin, k slippage and the like. These calculations may be performed with any currently known or future techniques.
  • processor(s) can be configured to combine these into a single stimulated area value.
  • This stimulated area value can, in some embodiments, be the stimulated area A (h* x f ) times the root of the effective permeability k.
  • the analytical model for gas may be based on an equivalent gas flow rate equation: (skin)(# of fracs)(Ap)(h)(3 ⁇ 4 gs )
  • K gas gas permeability
  • T reservoir temperature in degrees Farenheit
  • Ye drainage area half-length or half distance between fracs
  • the processor(s) can generate a log of dimensionless time values.
  • this model may assume and treat each fracture as a single vertical well with a single horizontal fracture.
  • the processor(s) handle each fracture in a well with multiple fractures as individual identical single fracture wells with a well length based on the original well length divided by the number of fractures.
  • the processor(s) can generate an analytical model including a log of dimensionless time and pressure estimates. From these logs and equation (1), a single phase gas forecast estimate can be provided for each time step. These cumulative gas volumes are at surface conditions prior to liquids being removed.
  • the relationship of pressure drop over time p/z can be used to calculate pressure steps as the gas is produced.
  • the processor(s) generate the p/z relationship based on initial pressure, and final pressure from the measured or calculated data.
  • an initial pressure can be a known measured data point, or it can be calculated based on depth and gradient data.
  • the gradient may be a varied parameter during the matching process.
  • each time interval can be divided into two or more subintervals.
  • the processor(s) can be configured to determine the pressure, P/Z slope and Q for each subinterval. The average pressure across these subintervals is then used as the pressure for the whole interval. In some examples, this can potentially provide a more accurate estimation of the changing pressure as the well ages.
  • the processor(s) can be configured to account for changes to the gas/liquid composition changes with pressure. Based on the condensate gas ratio parameter, the processor(s) can generate or access a database of PVT (pressure-volume- temperature) tables at different yield bands.
  • PVT pressure-volume- temperature
  • the processor(s) can generate an analytical model which alternatively or additional accounts for oil production.
  • Fig. 7 shows an example flowchart 700 outlining aspects of an example oil model.
  • the processor(s) generate or receive physical input values or ranges of physical input values based on obtained geological data.
  • geological data can be derived from petrophysical interpretation of well logs from adjacent or nearby wells.
  • the physical inputs can include time steps, pressure differences, net pay, effective permeability, porosity, drainage area half width, fracture spacing half distance, condensate gas ratio or gas oil ratio, initial water saturation and the like.
  • the processor(s) may calculate other inputs which may be dependent or otherwise correlated with the physical inputs or other calculated inputs. These calculated inputs or correlations may include oil viscosity, gas viscosity, oil expansion factor, gas expansion factor, bubble point pressure, oil compressibility, formation volume factors, solution gas-oil ratio, formation volume factor, oil expansion factor, and the like. In some examples, these calculated inputs may be calculated relative to a bubble point pressure. These calculations may be performed with any currently known or future techniques. [0099] As described above, the processor(s) can generate the oil analytical model by determining a stimulated area value (e.g. A root K) similar to the gas model. However, the dimensionless time value function for oil can be based on: r _ _ . occidental . 0.00633 *Green Function Time .
  • a stimulated area value e.g. A root K
  • Tda dimensionless time
  • h pay thickness or layer thickness * frac height
  • K ef f absolute effective permeability
  • K oil relative permeability to oil
  • ⁇ 0 gas viscosity
  • the processor(s) can generate an analytical oil model including a log of dimensionless time and pressure estimates. From these logs and equations, an oil forecast estimate can be provided for each time step. These cumulative gas volumes are at surface conditions prior to liquids being removed. [00116] In some embodiments, the processor(s) can apply material balance equations to adjust the oil production model to account for different gas-oil ratios at different pressures.
  • Absolute permeability is the measure of the ability of a single phase fluid to move through a porous medium. When multiple phases (ie. Gas and oil, or oil and water) are present at the same time inefficiencies are created resulting in a permeability that is a fraction of the absolute permeability. This is referred to as relative permeability. Relative permeability changes as a function of the saturation of one phase as a percentage of pore volume. Relative permeability curves describe this relationship. The curves can be determined through special core analysis or assumed based on an analog. In the oil production model, relative perm can be used to account for changes in productivity as the reservoir is depleted and gas is introduced out of solution. The relative permeability adjustment causes an appropriate reduction in productivity.
  • the processor(s) match the set of potential subterranean formation models to the production data for the well(s) based on an analytical fracture model.
  • the processor(s) can match the potential subterranean formation models based on both an oil analytical fracture model and a gas analytical fracture model.
  • each model can produce its own production models and associated stimulated area values.
  • the processor(s) can use the results of the two different models (gas or oil) to determine a probability that a portion or all of a subterranean formation will use one model over the other.
  • the processor(s) can store and/or compile all the production models and their corresponding error values and stimulated area values.
  • Fig. 8 shows a plot 800 of stimulated area vs. error with each data point representing a matched production model.
  • the processor(s) can be configured to generate such a plot and output it on an output device such as a display, printer, or communication device.
  • the processor(s) can be configured to identify a subset 810 of the production models (see Figs. 8 and 9) which have an error value below a defined threshold or which otherwise fit the production profile from the production data within a defined error threshold.
  • the processors generate a forecast production model.
  • the forecast production model is defined by a number of input parameters representative of geological characteristics of the subterranean formation.
  • the forecast production model is based on the stimulated area values identified by the analytical model and well matching.
  • the forecast production model can generate production forecasts over time for the area of interest.
  • ranges and/or distributions of input parameters for the area of interest can be used as inputs to generate an average production forecast for the area.
  • the forecasting model can be run deterministically using single values for each parameter or Monte Carlo or similar techniques can be used to deliver a distribution of production forecasts. Forecasts can be created for both hydrocarbon liquids and gas. [00124] In some embodiments, the forecast models can provide an indication of predicted average production rates over time. In other embodiments, the forecast models can provide probabilistic production rates based on the possible distributions for input parameters including the geological characteristics and the stimulated area value. As illustrated in Fig. 11 , in some embodiments, graphs are generated to illustrated different probabilistic production rates over time.
  • the system can, in some instance, model production rates which account for physical and potentially subtle geological variations across an area.
  • forecast models can be used to generate a visual map illustrating different production forecasts across a prospective area.
  • the forecast models can be used to generate a probabilistic financial viabilities of different potions of an area.
  • these forecast models can in some instances account for various unknowns in the available production data and/or variations in geological characteristics across a play.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Fluid Mechanics (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Algebra (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Environmental & Geological Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Operations Research (AREA)

Abstract

Des aspects de la présente invention peuvent concerner des dispositifs, des systèmes et des procédés pour modéliser la production de ressources pour laquelle il peut y avoir des informations incomplètes et/ou des paramètres inconnus. Dans certains modes de réalisation, le procédé comprend l'application d'un modèle de fracture analytique et la réduction d'un nombre de modèles à mettre en correspondance dans un ensemble de modèles de formation souterraine potentiels.
PCT/CA2016/050369 2016-03-30 2016-03-30 Procédés, systèmes et dispositifs permettant de modéliser les propriétés d'un réservoir WO2017165949A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US16/088,949 US20190120022A1 (en) 2016-03-30 2016-03-30 Methods, systems and devices for modelling reservoir properties
CA3019180A CA3019180A1 (fr) 2016-03-30 2016-03-30 Procedes, systemes et dispositifs permettant de modeliser les proprietes d'un reservoir
PCT/CA2016/050369 WO2017165949A1 (fr) 2016-03-30 2016-03-30 Procédés, systèmes et dispositifs permettant de modéliser les propriétés d'un réservoir

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CA2016/050369 WO2017165949A1 (fr) 2016-03-30 2016-03-30 Procédés, systèmes et dispositifs permettant de modéliser les propriétés d'un réservoir

Publications (1)

Publication Number Publication Date
WO2017165949A1 true WO2017165949A1 (fr) 2017-10-05

Family

ID=59962305

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2016/050369 WO2017165949A1 (fr) 2016-03-30 2016-03-30 Procédés, systèmes et dispositifs permettant de modéliser les propriétés d'un réservoir

Country Status (3)

Country Link
US (1) US20190120022A1 (fr)
CA (1) CA3019180A1 (fr)
WO (1) WO2017165949A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019110851A1 (fr) * 2017-12-08 2019-06-13 Solution Seeker As Modélisation de réseaux de pétrole et de gaz
US20200150305A1 (en) * 2018-11-14 2020-05-14 International Business Machines Corporation Machine learning platform for processing data maps
US11613957B1 (en) 2022-01-28 2023-03-28 Saudi Arabian Oil Company Method and system for high shut-in pressure wells
US12024985B2 (en) 2022-03-24 2024-07-02 Saudi Arabian Oil Company Selective inflow control device, system, and method

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2555803B (en) * 2016-11-09 2021-11-10 Equinor Energy As System and method for providing information on production value and/or emissions of a hydrocarbon production system
CA3043756C (fr) * 2016-12-29 2021-07-06 Exxonmobil Upstream Research Company Procede et systeme de regression et de classification dans des modeles de subsurface pour supporter la prise de decisions dans les operations d'exploitation d'hydrocarbures
NO20220126A1 (en) 2019-08-14 2022-01-24 Landmark Graphics Corp Processing hydrocarbon production data to characterise treatment effectiveness and landing zones
US11216926B2 (en) * 2020-02-17 2022-01-04 Halliburton Energy Services, Inc. Borehole image blending through machine learning
CN111583407B (zh) * 2020-06-05 2023-05-12 武汉正远岩土科技有限公司 一种高效的基于纸质钻孔的三维地质建模智能处理方法
US11933165B2 (en) * 2021-03-15 2024-03-19 Saudi Arabian Oil Company Hydraulic fracture conductivity modeling
WO2024197160A1 (fr) * 2023-03-22 2024-09-26 Schlumberger Technology Corporation Système autonome intégré de commande de production de champ pétrolifère

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080288226A1 (en) * 2000-02-22 2008-11-20 Gurpinar Omer M Integrated Resevoir optimization
US20120253770A1 (en) * 2010-02-12 2012-10-04 David Stern Method and System For Creating History Matched Simulation Models
WO2014189523A1 (fr) * 2013-05-24 2014-11-27 Halliburton Energy Services, Inc. Procédés et systèmes permettant de mettre en correspondance un historique de réservoir pour une meilleure évaluation des performances de réservoir

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080288226A1 (en) * 2000-02-22 2008-11-20 Gurpinar Omer M Integrated Resevoir optimization
US20120253770A1 (en) * 2010-02-12 2012-10-04 David Stern Method and System For Creating History Matched Simulation Models
WO2014189523A1 (fr) * 2013-05-24 2014-11-27 Halliburton Energy Services, Inc. Procédés et systèmes permettant de mettre en correspondance un historique de réservoir pour une meilleure évaluation des performances de réservoir

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019110851A1 (fr) * 2017-12-08 2019-06-13 Solution Seeker As Modélisation de réseaux de pétrole et de gaz
US11795787B2 (en) 2017-12-08 2023-10-24 Solution Seeker As Modelling of oil and gas networks
US20200150305A1 (en) * 2018-11-14 2020-05-14 International Business Machines Corporation Machine learning platform for processing data maps
US11719842B2 (en) * 2018-11-14 2023-08-08 International Business Machines Corporation Machine learning platform for processing data maps
US11613957B1 (en) 2022-01-28 2023-03-28 Saudi Arabian Oil Company Method and system for high shut-in pressure wells
US12024985B2 (en) 2022-03-24 2024-07-02 Saudi Arabian Oil Company Selective inflow control device, system, and method

Also Published As

Publication number Publication date
US20190120022A1 (en) 2019-04-25
CA3019180A1 (fr) 2017-10-05

Similar Documents

Publication Publication Date Title
US20190120022A1 (en) Methods, systems and devices for modelling reservoir properties
US10767448B2 (en) Multistage oilfield design optimization under uncertainty
Liang et al. Subsurface well spacing optimization in the Permian Basin
US9910938B2 (en) Shale gas production forecasting
Miller et al. Applying innovative production modeling techniques to quantify fracture characteristics, reservoir properties, and well performance in shale gas reservoirs
US20160253767A1 (en) Designing Wellbore Completion Intervals
Gherabati et al. Assessment of hydrocarbon in place and recovery factors in the Eagle Ford Shale play
Saputelli et al. Design optimization of horizontal wells with multiple hydraulic fractures in the Bakken Shale
Yang et al. Rapid multistage hydraulic fracture design and optimization in unconventional reservoirs using a novel Fast Marching Method
Cook et al. Unconventional asset development work flow in the Eagle Ford Shale
US20230111179A1 (en) Predicting oil and gas reservoir production
EP3596515A1 (fr) Inversion de niveau d'eau libre fondée sur le type de roche
Barba Liquids Rich Organic Shale Recovery Factor Application
Molinari et al. A Reduced Physics Modeling Approach to Understand Multiphase Well Production Performance for Unconventional Reservoirs
Jin et al. Quantitative characterization of bound and movable fluid microdistribution in porous rocks using nuclear magnetic resonance
US11493654B2 (en) Construction of a high-resolution advanced 3D transient model with multiple wells by integrating pressure transient data into static geological model
CA2818464C (fr) Prevision de production de gaz de schiste
Suarez et al. Fracturing-to-Production Simulation Approach for Completion Optimization in the Vaca Muerta Shale
Collins et al. Addressing Forecasting Non-Uniqueness and Uncertainty in Unconventional Reservoir Systems Using Experimental Design
Wallace et al. Understanding Completion Performance in Niobrara-Codell Reservoirs Through the Use of Innovative Software-Guided Workflows and Models
Gherabati Material-balance approach for determining drainage volume of multifracture unconventional oil wells
Mata et al. Hydraulic Fracture Treatment, Optimization, and Production Modeling
Beohar et al. Integrating pressure transient and rate transient analysis for eur estimation in tight gas volcanic reservoirs
Ganiev et al. Waterflood Optimization Planning Based on MRT Analysis of Long-Term Bottom-Hole Pressure Records
Vassilellis et al. The Role of Drive Mechanisms in Redesigning Development Practices in a Fractured Tight Oil Carbonate Resource

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 3019180

Country of ref document: CA

NENP Non-entry into the national phase

Ref country code: DE

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16895763

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 16895763

Country of ref document: EP

Kind code of ref document: A1