EP2795528A2 - Systèmes et procédés d'évaluation de durées de percée de fluide des emplacements de puits de production - Google Patents

Systèmes et procédés d'évaluation de durées de percée de fluide des emplacements de puits de production

Info

Publication number
EP2795528A2
EP2795528A2 EP12868041.0A EP12868041A EP2795528A2 EP 2795528 A2 EP2795528 A2 EP 2795528A2 EP 12868041 A EP12868041 A EP 12868041A EP 2795528 A2 EP2795528 A2 EP 2795528A2
Authority
EP
European Patent Office
Prior art keywords
streamline
grid
shortest
cell
fastest
Prior art date
Legal status (The legal status 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 status listed.)
Withdrawn
Application number
EP12868041.0A
Other languages
German (de)
English (en)
Other versions
EP2795528A4 (fr
Inventor
Maucec MARKO
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Landmark Graphics Corp
Original Assignee
Landmark Graphics Corp
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 Landmark Graphics Corp filed Critical Landmark Graphics Corp
Publication of EP2795528A2 publication Critical patent/EP2795528A2/fr
Publication of EP2795528A4 publication Critical patent/EP2795528A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • 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
    • E21B47/00—Survey of boreholes or wells
    • E21B47/10—Locating fluid leaks, intrusions or movements
    • 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
    • E21B43/16—Enhanced recovery methods for obtaining hydrocarbons
    • E21B43/20—Displacing by water
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Definitions

  • the present invention generally relates to estimating fluid breakthrough times at producing well locations. More particularly, the invention relates to estimating fluid breakthrough times at producing well locations based on fluid propagation simulations.
  • HM History Matching
  • HM is a systematic procedure of altering a reservoir simulation model to reproduce the dynamic field response.
  • the main objectives are a) integration of production data into reservoir models; b) flexibility, cost- effectiveness and computational efficiency; and c) full utilization of dynamic data.
  • HM technology has evolved tremendously and gained major recognition and expansion from the traditional (i.e. manual, deterministic) approach, mostly built on stratigraphic methods to new developments like probabilistic, streamline-based HM, sensitivity/gradient-based and experimental design.
  • HM workflows largely consider the minimization of the misfit between the measured and simulated fluid (e.g. oil or water) dynamic response at the individual production well as one of the inversion main objectives.
  • the response misfit represents the differential or cumulative water-cut curves with two main attributes: 1) fluid breakthrough time; and 2) trend and shape of the response. While both attributes represent important variables in the process of misfit minimization, it is the fluid breakthrough time that bares the highest impact on the economics of the well production.
  • the interval (i.e. time-frame) of the fluid breakthrough is always burdened with uncertainty, which makes the effort of estimation with highest confidence possible, even more relevant.
  • it is a good practice in HM of dynamic well data to consider the breakthrough time as the first-order effect and the variations in curve trend/shape as the second-order effect, because they mainly reflect on the operating conditions.
  • the production data may be noisy and inherently biased.
  • the present invention therefore, meets the above needs and overcomes one or more deficiencies in the prior art by providing systems and methods for estimating fluid breakthrough times at producing well locations based on fluid propagation simulations.
  • the present invention includes a method for estimating a fluid breakthrough time at a production well based on fluid propagation simulation data, comprising: i) identifying streamline tracking data; ii) calculating an average streamline travel time in each grid-cell based on the streamline tracking data; iii) identifying a shortest or fastest streamline for the production well using the average streamline travel time in each grid-cell; iv) calculating an average time-of-flight for the shortest or fastest streamline over each traversed grid-cell using a computer processor; and v) estimating the fluid breakthrough time at the production well using the fluid propagation simulation data, and the average time-of-flight for the shortest or fastest streamline.
  • the present invention includes a non-transitory program carrier device tangibly carrying computer executable instructions for estimating a fluid breakthrough time at a production well.
  • the instructions being executable to implement: i) identifying streamline tracking data; ii) calculating an average streamline travel time in each grid-cell based on the streamline tracking data; iii) identifying a shortest or fastest streamline for the production well using the average streamline travel time in each grid-cell; iv) calculating an average time-of-flight for the shortest or fastest streamline over each traversed grid-cell using; and v) estimating the fluid breakthrough time at the production well using the fluid propagation simulation data, and the average time-of-flight for the shortest or fastest streamline.
  • FIG. 1 is a flow diagram illustrating one embodiment of a method for implementing the present invention.
  • FIG. 2A illustrates the velocity and the direction of fluid propagating through a wide sand pocket.
  • FIG. 2B illustrates the velocity and the direction of fluid propagating through a narrow sand pocket.
  • FIG. 3 illustrates an example of fluid propagation through a sand fraction of a facies model during the initial stage of simulation.
  • FIG. 4A illustrates a synthetic 2D permeability model with 2500 grid-cells (50x50) and a 5 -spot-pattern of wells (linjection well (I) and 4 production wells (Pi-P 4 )).
  • Fig. 4B illustrates a simulation of fluid propagation through the 2D permeability model in FIG. 4A from the injector well (I) in terms of the number of iterations (2500) that the simulation was run.
  • FIG. 5 illustrates a possible streamline distribution in the 5-spot-pattern of wells in FIG. 4B.
  • FIG. 6 illustrates the streamline travel time along its arc length within a given grid-cell (i,j,k) of a 2D permeability model.
  • FIG. 7A illustrates the observed (measured) water-cut curve for the producing well Pi in FIG. A.
  • FIG. 7B illustrates the observed (measured) water-cut curve for the producing well P 2 in FIG. 4A.
  • FIG. 7C illustrates the observed (measured) water-cut curve for the producing well P 3 in FIG. 4A.
  • FIG. 7D illustrates the observed (measured) water-cut curve for the producing well P 4 in FIG. 4A.
  • FIG. 8 is a block diagram illustrating one embodiment of a system for implementing the present invention.
  • the present invention includes systems and methods to estimate fluid breakthrough times at producing well locations based on the simulation of fluid propagation.
  • the present invention includes a fluid propagation simulation, which is generally static and renders the invasion time(s) for fluid injected at the injection well(s) to reach the production well(s).
  • the simulation affords full consideration of facies modeling, which preserves control over depositional continuity of geological models by directly constraining the simulation with the facies distribution.
  • the simulation also preserves the stochasticity of the fluid front propogation. Despite the static nature of the simulation, the stochastic sampling of the moving fluid front is performed by using a uniform distribution.
  • the present invention converts the fluid invasion time(s) (given by the simulation in units of iterations) to the domain of physical time (given i.e. in days, weeks, months%), which is compatible with the well production history.
  • the present invention therefore, provides new possibilities for the rapid estimation of valuable well production parameters in a fast and cost- effective manner. For example, a fast and accurate estimation of the fluid breakthrough time(s) associated with an individual reservoir model can be achieved prior to commencing a full inversion. Such an estimate would provide valuable information to the well operators in terms of well valve dynamics, particularly in water/gas-flooding EOR projects where the management of oil and water/gas production bares a substantial economic impact.
  • the present invention uses the combination of streamline tracking and associated Time-Of-Flight ("TOF") with the simulation.
  • TOF Time-Of-Flight
  • the present invention therefore, enables quick approximation of fluid breakthrough times following the simulation run and one iteration of streamline tracking in the process of streamline-sensitivity assisted Automated History Matching ("AHM”) of reservoir models.
  • AHM Automated History Matching
  • FIG. 1 a flow diagram illustrates one embodiment of a method 100 for implementing the present invention.
  • step 102 fluid propagation simulation
  • FPS fluid propagation simulation
  • One technique for performing FPS is based on an algorithm in the RGeoS software package developed by D. Renard.
  • the FPS algorithm simulates the distribution of several fluids known at the injection and/or production wells, which is conditioned by the facies information known at the nodes of a regular grid and tends to let the fluid encountered at the wells (e.g. the injection well) to grow or expand spatially.
  • the velocity and the direction of growth depend on the size of the sand pockets that can be filled. In FIG. 2, for example, true velocity and the direction of fluid propagating through a wide sand pocket (FIG. 2A) and a narrow sand pocket (FIG. 2B) are illustrated.
  • Velocity vectors 202, 204 are utilized in the FPS algorithm.
  • the FPS algorithm is designed to perform one simulation of a numerical variable using the Eden simulation technique.
  • the technique provides a faster alternative solution for a multiphase fluid flow simulation program.
  • the technique combines a dual medium "black and white" example where white represents sand and black represents shale with one or more injection wells and one or more production wells as illustrated in FIG. 3. In this example, the locations of sand facies 302, 304, 306, and of two injection wells 307, 308, are illustrated.
  • FIG. 4 A a synthetic 2D permeability model is illustrated with 2500 grid cells (50x50) and a 5- spot- pattern of wells (1 injection well (I) and 4 production wells (P1-P4)).
  • the FPS algorithm was executed in 2500 iterations because one cell of the model is populated per iteration.
  • FIG. 4B a simulation of fluid propagation through the 2D permeability model in FIG. 4A from the injection well (I) is illustrated in terms of the number of iterations (2500) the simulation was run.
  • FIG. 5 one possible streamline distribution in the 5- spot-pattern of wells in FIG. 4B is illustrated.
  • tracking TOF from the production well(s) indicates the drainage volume; and iii) tracking fluid from the injection well gives an assessment of swept volume.
  • step 104 the FPS data results from step 102 are identified, which includes the fluid invasion time given by the number of simulation iterations needed for the fluid to reach any production well (P m ) from an injection well through one or more grid-cells representing the reservoir property model.
  • the streamline tracking data are identified using any well known technique, which include the number of streamline segments traversing each grid-cell (NSLN), the travel time ( ⁇ ) for each streamline segment ( ⁇ ];, ⁇ dress ) in each grid-cell, the grid-cell indices and the total number of grid-cells traversed by all streamlines connecting an injection well with a production well.
  • NSLN grid-cell
  • ⁇ travel time
  • ⁇ travel time
  • ⁇ corresponds to the "slowness" of the streamline tracer (defined as the inverse of the tracer velocity) and dr corresponds to the arc length of the streamline segment ( ⁇ ,' ⁇ ⁇ ) between the inlet and outlet locations on the bounding surface of the grid-cell with (i,j,k) coordinates.
  • step 108 the average streamline travel time in each grid-cell ( Of ) is calculated by taking into account all streamline segments traversing each grid-cell, which may be calculated using the following equation: where (NSLN) is the number of streamline segments traversing each grid-cell from step 106 and is the travel time for each streamline segment in each grid-cell from step 106.
  • step 114 the shortest/fastest streamline is identified for each production well
  • the shortest/fastest streamline is the streamline with the lowest sum of average streamline travel times (9f min ) in the grid-cells the streamline traverses between an injection well (I) and a production well (P m ).
  • step 116 the total number of all grid-cells (N GC mm ) traversed by the shortest/fastest streamline identified in step 114, and their indices from step 106, are stored.
  • step 118 the average TOF ( ⁇ TOF> min ) for the shortest/fastest streamline identified in step 114 is calculated over each traversed grid-cell using the lowest sum of average streamline travel times (df mm ) for the shortest/fastest streamline identified in step 114 and the total number of all grid-cells ( Gc min ) stored in step 116, which may be calculated using the following equation: ⁇ 3r ⁇ u min (3) J where index (u) represents the number of runs over all indices of grid-cells traversed by the shortest/fastest streamline.
  • the distinction between the "fastest” and the “slowest” streamline from the distribution of streamlines associated with each production well (P m ) is relevant to discriminate between the homogeneous and heterogeneous spatial distribution of reservoir properties such as, for example, channels.
  • the difference between the distribution of streamlines in FIG. 5 reveals that production wells P 2 and P 3 are connected with injection well (I) through a distinctively different geological formation than production wells Pi and P 4) which might correspond to an underlying channel structure.
  • step 120 the method 100 determines if all grid-cells traversed by the shortest/fastest streamline have been considered. If all traversed grid-cells have not been considered, then the method 100 returns to step 118. If all traversed grid-cells have been considered, then the method 100 proceeds to step 124. Alternatively, steps 118 through 120 may be performed at the same time for each traversed grid-cell.
  • step 124 an estimate of the fluid breakthrough time for each production well (P m ) is calculated by combining the streamline tracking data from step 106 with the FPS data from step 104, which may be calculated using the following equation:
  • N xyz ) and (N p ) represent the total size of the reservoir property model and the total number of production wells, respectively
  • ( ⁇ TOF> min ) represents the average TOF for the shortest/fastest streamline calculated in step 118
  • NTM LN represents the total number of grid- cells traversed by all streamlines connecting injection well (I) with a production well (P m ) and ⁇ INV ) re P res ents the fluid invasion time from step 104.
  • step 126 the method 100 determines if all production wells have been considered. If all production wells (P m ) have not been considered, then the method 100 returns to step 104. If all production wells (P m ) have been considered, then the method 100 ends. Alternatively, steps 104 through 126 may be performed at the same time for each production well (P m ).
  • FIGS. 7A, 7B, 7C, and 7D the observed (measured) water-cut curves for the configuration model in FIG 4A are given in FIGS. 7A, 7B, 7C, and 7D for each of the four production wells (Pi, P 2 , P 3 , and P 4 ).
  • Table 2 lists the water invasion times calculated by the FPS algorithm the water breakthrough times (T B7 ) calculated using the proposed method in FIG. 1 and the uncertainty associated with result obtained by the proposed method in FIG. 1.
  • the present invention may be implemented through a computer-executable program of instructions, such as program modules, generally referred to software applications or application programs executed by a computer.
  • the software may include, for example, routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types.
  • DecisionSpace® Desktop which is a commercial software application marketed by Landmark Graphics Corporation, may be used as an interface application to implement the present invention.
  • the software may also cooperate with other code segments to initiate a variety of tasks in response to data received in conjunction with the source of the received data.
  • the software may be stored and/or carried on any variety of memory such as CD-ROM, magnetic disk, bubble memory and semiconductor memory (e.g., various types of RAM or ROM).
  • the software and its results may be transmitted over a variety of carrier media such as optical fiber, metallic wire, and/or through any of a variety of networks, such as the Internet.
  • the invention may be practiced with a variety of computer-system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable-consumer electronics, minicomputers, mainframe computers, and the like. Any number of computer- systems and computer networks are acceptable for use with the present invention.
  • the invention may be practiced in distributed-computing environments where tasks are performed by remote- processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer- storage media including memory storage devices.
  • the present invention may therefore, be implemented in connection with various hardware, software, or a combination thereof, in a computer system or other processing system.
  • FIG. 8 a block diagram illustrates one embodiment of a system for implementing the present invention on a computer.
  • the system includes a computing unit, sometimes referred to as a computing system, which contains memory, application programs, a client interface, a video interface, and a processing unit.
  • the computing unit is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention.
  • the memory primarily stores the application programs, which may also be described as program modules containing computer-executable instructions, executed by the computing unit for implementing the present invention described herein and illustrated in FIG. 2.
  • the memory therefore, includes a fluid breakthrough time estimating module, which enables the methods illustrated and described in reference to FIG. 1 and integrates functionality from the remaining application programs illustrated in FIG. 8.
  • the fluid breakthrough time estimating module may be used to execute many of the functions described in reference to the method 100 in FIG. 1.
  • Decision Space® Desktop may be used, for example, as an interface application to implement the fluid breakthrough time estimating module and to utilize the results of the method 100 in FIG. 1.
  • the computing unit typically includes a variety of computer readable media, By way of example, and not limitation, computer readable media may comprise computer storage media
  • the computing system memory may include computer storage media in the form of volatile and/or nonvolatile memory such as a read only memory (ROM) and random access memory (RAM).
  • ROM read only memory
  • RAM random access memory
  • a basic input/output system (BIOS) containing the basic routines that help to transfer information between elements within the computing unit, such as during start-up, is typically stored in ROM.
  • the RAM typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by the processing unit.
  • the computing unit includes an operating system, application programs, other program modules, and program data.
  • the components shown in the memory may also be included in other removable/non-removable, volatile/nonvolatile computer storage media or they may be implemented in the computing unit through an application program interface ("API") or cloud computing, which may reside on a separate computing unit connected through a computer system or network.
  • API application program interface
  • a hard disk drive may read from or write to nonremovable, nonvolatile magnetic media
  • a magnetic disk drive may read from or write to a removable, non-volatile magnetic disk
  • an optical disk drive may read from or write to a removable, nonvolatile optical disk such as a CD ROM or other optical media.
  • removable/non-removable, volatile/non-volatile computer storage media may include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like.
  • the drives and their associated computer storage media discussed above provide storage of computer readable instructions, data structures, program modules and other data for the computing unit.
  • a client may enter commands and information into the computing unit through the client interface, which may be input devices such as a keyboard and pointing device, commonly referred to as a mouse, trackball or touch pad. Input devices may include a microphone, joystick, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit through a system bus, but may be connected by other interface and bus structures, such as a parallel port or a universal serial bus (USB).
  • a monitor or other type of display device may be connected to the system bus via an interface, such as a video interface.
  • a graphical user interface (“GUI") may also be used with the video interface to receive instructions from the client interface and transmit instructions to the processing unit.
  • computers may also include other peripheral output devices such as speakers and printer, which may be connected through an output peripheral interface.

Landscapes

  • Geology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Mining & Mineral Resources (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Geophysics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Compounds Of Alkaline-Earth Elements, Aluminum Or Rare-Earth Metals (AREA)
  • Solid-Sorbent Or Filter-Aiding Compositions (AREA)

Abstract

Systèmes et procédés d'évaluation de durées de percée de fluide à des emplacements de puits de production sur la base d'une simulation de propagation de fluide.
EP12868041.0A 2012-02-10 2012-02-10 Systèmes et procédés d'évaluation de durées de percée de fluide des emplacements de puits de production Withdrawn EP2795528A4 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2012/024656 WO2013119248A2 (fr) 2012-02-10 2012-02-10 Systèmes et procédés d'évaluation de durées de percée de fluide des emplacements de puits de production

Publications (2)

Publication Number Publication Date
EP2795528A2 true EP2795528A2 (fr) 2014-10-29
EP2795528A4 EP2795528A4 (fr) 2016-06-29

Family

ID=48948146

Family Applications (1)

Application Number Title Priority Date Filing Date
EP12868041.0A Withdrawn EP2795528A4 (fr) 2012-02-10 2012-02-10 Systèmes et procédés d'évaluation de durées de percée de fluide des emplacements de puits de production

Country Status (10)

Country Link
US (1) US20150039276A1 (fr)
EP (1) EP2795528A4 (fr)
CN (1) CN104067290A (fr)
AR (1) AR089973A1 (fr)
AU (1) AU2012369161B2 (fr)
BR (1) BR112014017652A8 (fr)
CA (1) CA2863156A1 (fr)
MX (1) MX2014008897A (fr)
RU (1) RU2590265C2 (fr)
WO (1) WO2013119248A2 (fr)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015112211A1 (fr) * 2014-01-24 2015-07-30 Landmark Graphics Corporation Acidification optimisée d'un puits de production à proximité d'un aquifère
US10191182B2 (en) 2015-12-01 2019-01-29 Saudi Arabian Oil Company Accuracy of water break-through time prediction
CA3051501A1 (fr) 2017-01-26 2018-08-02 Exa Corporation Visualisations d'ecoulements a phases multiples basees sur un temps d'occupation de fluide
US11714040B2 (en) 2018-01-10 2023-08-01 Dassault Systemes Simulia Corp. Determining fluid flow characteristics of porous mediums
US10519768B2 (en) * 2018-02-21 2019-12-31 Saudi Arabian Oil Company Systems and methods for operating hydrocarbon wells to inhibit breakthrough based on reservoir saturation
US11530598B2 (en) 2018-08-21 2022-12-20 Dassault Systemes Simulia Corp. Determination of oil removed by gas via miscible displacement in reservoir rock
CN109902329B (zh) * 2018-09-21 2023-06-02 长江大学 一种油藏模拟辅助历史拟合方法、系统、存储介质及设备
US10983233B2 (en) 2019-03-12 2021-04-20 Saudi Arabian Oil Company Method for dynamic calibration and simultaneous closed-loop inversion of simulation models of fractured reservoirs
EP3976923A4 (fr) * 2019-05-28 2022-12-21 Services Pétroliers Schlumberger Création d'un système de complétion basé sur des lignes de courant
US11847391B2 (en) 2020-06-29 2023-12-19 Dassault Systemes Simulia Corp. Computer system for simulating physical processes using surface algorithm
US11907625B2 (en) 2020-12-29 2024-02-20 Dassault Systemes Americas Corp. Computer simulation of multi-phase and multi-component fluid flows including physics of under-resolved porous structures
CN117722164B (zh) * 2024-02-18 2024-04-16 西南石油大学 一种有水气藏均匀水侵控制方法

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2842321B1 (fr) * 2002-07-11 2008-12-05 Inst Francais Du Petrole Methode pour contraindre un champ de permeabilite heterogene representant un reservoir souterrain par des donnees dynamiques
US7289942B2 (en) 2003-03-26 2007-10-30 Exxonmobil Upstream Research Company Performance prediction method for hydrocarbon recovery processes
US20080167849A1 (en) * 2004-06-07 2008-07-10 Brigham Young University Reservoir Simulation
CN102124185B (zh) * 2007-12-07 2014-01-29 兰德马克绘图国际公司，哈里伯顿公司 使用基于单元的流动模拟结果计算流线轨迹的系统和方法
US20100312535A1 (en) * 2009-06-08 2010-12-09 Chevron U.S.A. Inc. Upscaling of flow and transport parameters for simulation of fluid flow in subsurface reservoirs
US8775142B2 (en) * 2010-05-14 2014-07-08 Conocophillips Company Stochastic downscaling algorithm and applications to geological model downscaling
US8646525B2 (en) * 2010-05-26 2014-02-11 Chevron U.S.A. Inc. System and method for enhancing oil recovery from a subterranean reservoir

Also Published As

Publication number Publication date
BR112014017652A2 (fr) 2017-06-20
AU2012369161A1 (en) 2014-07-24
CA2863156A1 (fr) 2013-08-15
EP2795528A4 (fr) 2016-06-29
US20150039276A1 (en) 2015-02-05
WO2013119248A3 (fr) 2014-04-17
AU2012369161B2 (en) 2015-05-28
WO2013119248A2 (fr) 2013-08-15
MX2014008897A (es) 2014-09-22
CN104067290A (zh) 2014-09-24
AR089973A1 (es) 2014-10-01
RU2590265C2 (ru) 2016-07-10
RU2014130786A (ru) 2016-04-10
BR112014017652A8 (pt) 2017-07-11

Similar Documents

Publication Publication Date Title
AU2012369161B2 (en) Systems and methods for estimating fluid breakthrough times at producing well locations
US9576084B2 (en) Generating a smooth grid for simulating fluid flow in a well system environment
RU2592751C1 (ru) Геометрическое представление плоскостей развития трещин
US11899161B2 (en) Optimization under uncertainty for integrated models
US9836561B2 (en) Optimizing multistage hydraulic fracturing design based on three-dimensional (3D) continuum damage mechanics
US20160326845A1 (en) Multistage Oilfield Design Optimization Under Uncertainty
US9188699B2 (en) Basin-to reservoir modeling
WO2016018426A1 (fr) Estimation du rendement de production de puits dans des systèmes de réservoir fracturé
AU2013393321B2 (en) Hybrid approach to assisted history matching in large reservoirs
AU2013392090B2 (en) Systems and methods for optimizing existing wells and designing new wells based on the distribution of average effective fracture lengths
Olalotiti-Lawal et al. Post-combustion CO2 WAG pilot in a mature field: model calibration and optimization
CA2866156A1 (fr) Reperage de risques geomecaniques potentiels pendant une injection d'eau
AU2012396846B2 (en) System, method and computer program product for evaluating and ranking geobodies using a Euler Characteristic
Rezaei et al. Applications of Machine Learning for Estimating the Stimulated Reservoir Volume (SRV)
Puyang et al. An integrated modeling approach for natural fractures and posttreatment fracturing analysis: A case study
Wang et al. Hierarchical stochastic modeling and optimization for petroleum field development under geological uncertainty
Narumi et al. Demonstration of a Practical Ensemble-Based Reservoir Modelling Workflow for Development Planning on the Ichthys Gas-Condensate Field
Li et al. An effective model of near-well complex fracture network after hydraulic fracturing

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20140722

AK Designated contracting states

Kind code of ref document: A2

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAX Request for extension of the european patent (deleted)
A4 Supplementary search report drawn up and despatched

Effective date: 20160531

RIC1 Information provided on ipc code assigned before grant

Ipc: G06G 7/48 20060101AFI20160524BHEP

Ipc: E21B 49/00 20060101ALI20160524BHEP

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: EXAMINATION IS IN PROGRESS

17Q First examination report despatched

Effective date: 20190606

GRAP Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOSNIGR1

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: GRANT OF PATENT IS INTENDED

RIC1 Information provided on ipc code assigned before grant

Ipc: E21B 47/10 20120101ALI20191106BHEP

Ipc: E21B 49/00 20060101ALI20191106BHEP

Ipc: E21B 43/20 20060101AFI20191106BHEP

INTG Intention to grant announced

Effective date: 20191127

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20200603