WO2014207197A1 - Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir - Google Patents
Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir Download PDFInfo
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- WO2014207197A1 WO2014207197A1 PCT/EP2014/063689 EP2014063689W WO2014207197A1 WO 2014207197 A1 WO2014207197 A1 WO 2014207197A1 EP 2014063689 W EP2014063689 W EP 2014063689W WO 2014207197 A1 WO2014207197 A1 WO 2014207197A1
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- Prior art keywords
- records
- porosity
- facies
- data base
- values
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- 239000004215 Carbon black (E152) Substances 0.000 title claims abstract description 27
- 229930195733 hydrocarbon Natural products 0.000 title claims abstract description 27
- 150000002430 hydrocarbons Chemical class 0.000 title claims abstract description 27
- 238000000034 method Methods 0.000 title claims description 29
- 208000035126 Facies Diseases 0.000 claims description 39
- 238000009826 distribution Methods 0.000 claims description 26
- 239000000463 material Substances 0.000 claims description 20
- 230000035699 permeability Effects 0.000 claims description 18
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012896 Statistical algorithm Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000007619 statistical method Methods 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 2
- 238000012805 post-processing Methods 0.000 claims 2
- 239000006185 dispersion Substances 0.000 claims 1
- 230000006870 function Effects 0.000 description 11
- 238000003860 storage Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000000644 propagated effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001143 conditioned effect Effects 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Definitions
- the present invention is related to generating scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir, and more particularly to automatically supplying missing parameters and an uncertainty associated with each supplied parameter allowing to valuating the target hydrocarbon reservoir.
- Determining whether investing in a new hydrocarbon reservoir candidate is a good business decision depends on the inherent value of the reservoir. Factors determining the inherent value of the reservoir include, for example, the total amount of material that is ultimately recoverable from each new hydrocarbon reservoir (production potential), market prices (oil and/or natural gas prices) and the cost of recovering that material, or capture difficulty. Until the material is actually recovered, however, that inherent value can only be estimated from different, primarily known, reservoir prop- erties.
- Some investment candidate estimates have been based on identifying existing reser- voirs with properties that match or closely match (i.e., are similar to) the new, candidate reservoir.
- the closest existing reservoirs are known as "analogous reservoirs.”
- one or more experts e.g., geologists and reservoir engineers
- identified and selected analogous reservoirs based solely on experience and known or available investment candidate properties.
- Relevant information of such analogous reservoirs are stored in data bases having records with information on volumetric, facies and properties distribution, wherein all records not necessarily have all properties.
- the specialized literature shows continuously increased interest in analogues and its predictions.
- Sophisticated data mining and machine learning algorithms allow estima- tion of properties and such estimations also define the degree of uncertainty which can be reliably used within geostatistical workflows.
- Example can be found on the usage of reservoir analogous in the context of reservoir drive mechanism, recovery factor or reserve estimation and classification. Insufficient information, however, can make selecting analogous reservoirs guesswork at best, and make estimating the value error prone and uncertain.
- a mis-valuation could lead to wasted resources, e.g., from passing on an undervalued reservoir to exploit an overvalued reservoir. Missing parameters increase uncertainty and the likelihood of a mis-valuation.
- the present invention solves the problems providing a method to generate scenarios of hydrocarbon reservoirs when only limited amount of information on a target is available. Ranking reservoirs with different level of associated uncertainty within the same framework becomes key to make robust and unbiased decisions.
- the method according to the present invention allows assessing not only the geological parameters not known a priory with their associated uncertainty but also the geological and structural uncertainty of the reservoir with an accurate propagation of the inherent uncer- tainty.
- geological information comes from well logs, cores analysis, seismic data, etc. and production data can also be used to mitigate the uncertainty however in absence of these the geological scenario becomes hard to predict.
- geological information comprises scalar values lacking of spatial distributions as a three-dimensional structure.
- the method according to present invention generates scenarios of hydrocarbon reservoirs having a three-dimensional structure even if departing from scalar values.
- the method to generate scenarios of hydrocarbon reservoirs compiles information on the target hydrocarbon reservoir. Such information provides a concept model for the whole domain of the scenario. This information comprises scalar properties and may also comprise uncertainty data such as minimum and maximum values of some scalar data.
- the method requires the proposal of an integer number n of facies.
- the method provides automatically the partial rate f it and a normal distribution of the porosity having a mean value ⁇ and the cutoff values ⁇ TM 171 and ⁇ ⁇ for a predetermined threshold such that the area of the tail of the normal distribution is deemed to be neglectible beyond such threshold.
- Such variables are provided by solving an optimization problem that distributes the normal distribution of each facies in such a way the concept model is reproduced.
- the concept model is characterized by the number of facies and ⁇ TM ⁇ , ⁇ TM ⁇ ⁇ ' , and the ⁇ ⁇ ⁇ values of porosity of the target hydrocarbon reservoir.
- the method mainly uses the computed partial rate ⁇ to populate a plurality of three- dimensional scenarios having a three-dimensional domain and the shape of the n facies by means of a multipoint statistical method, preferably and not limited to a Sequential Indicator Simulation.
- Figure 1 This figure shows an example of a domain having three facies located within the domain.
- Figure 2 This figure shows an example of a schematic representation of an analogous data base wherein selected records have been selected when comparing the information of the target according to a predefined criterion.
- Figure 3 This figure shows an example of the distribution of the porosity for the concept model and how the normal distribution for each facies is distributed reproducing the concept model by solving an optimization problem.
- Figure 4 This figure shows an example of the set of scenarios generated in each property calculation providing a first generation and a second generation of scenarios.
- aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
- the computer readable medium may be a computer readable signal medium or a computer readable storage medium.
- a computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the fore- going.
- a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
- a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof.
- a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
- Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
- Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, in- eluding an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
- the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server.
- the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
- LAN local area network
- WAN wide area network
- Internet Service Provider for example, AT&T, MCI, Sprint, EarthLink, MSN, GTE, etc.
- These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
- the computer program instructions may also be loaded onto a computer, other pro- grammable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
- Figure 1 shows an example of an a prospective field for evaluation according to a preferred embodiment of the present invention, which begins by compiling information on the target hydrocarbon. This in- formation may be interpreted by a skilled person who may provide the number n of facies from this collected information. The number of facies is preferably between 1 and 8.
- Figure 1 shows a field having a domain (D) under certain portion of its surface, the area identified with letter A. This figure shows a domain (D) having three facies F lt F 2 F 3 distributed within the domain.
- the reservoir domain (D) is considered as a hexahedral shape. Structural uncertainty is considered on both shape and volume. In the present case since the conceptual model is a regular shape the uncertainty is represented by the reservoir area and thickness and the width-over-length ratio.
- the number of facies and other properties may be obtained comparing the target information with the analog data base.
- the partial rate ⁇ satisfies 1 being n the number of facies already fixed.
- the whole domain is modeled by a ⁇ TM ⁇ , ⁇ TM ⁇ ⁇ ' , and ⁇ ⁇ ⁇ val- ues which may be provided by a skilled person or the estimation may be obtained from the analogues data base, or more briefly "the analogues”.
- has been implemented as the Euclidean norm.
- the objective function to be minimized represents the error between the estimated property as a function of facies type and the value provided for instance by the analogues.
- MILP Mixed Integer Linear Programming
- the reference values ⁇ TM ⁇ , ⁇ TM ⁇ ⁇ ' , and ⁇ ⁇ ⁇ are obtained from the analogues data base (2).
- said information When compiling information on the target (1) hydrocarbon reservoir, said information at least have a material property among the material properties comprised in the records of the analog data base.
- the reference values are determined as follows: 1.
- at least one record further comprises the porosity of the material, that is, the minimum value ⁇ TMTM of the porosity, the mean value ⁇ ⁇ of the porosity, and the maximum value ⁇ £ ⁇ of the porosity.
- such information has at least a material property among the material properties comprised in the records of the analog data base.
- the similarity criterion is the criteria to define which records of the analogues data base are close to the target (1). This criterion may even combine properties or correlation between two or more properties.
- the plurality of three-dimensional scenarios having a three-dimensional domain is obtained generating the dimensions of said domain randomly. If the analogues have volumetric data such as the reservoir area and thickness, NTG (net-to-gross) and saturation information, then the maximum capacity to store oil within the domain is calculated. If the randomly generated domain (D) is not big enough to store the calculated volume of oil then said domain (D) is discarded.
- volumetric data such as the reservoir area and thickness, NTG (net-to-gross) and saturation information
- OOIP Oleal oil in place
- V is the volume of the domain
- ⁇ is the mean value of the porosity
- NTG is the mean value of the net-to-gross value
- S is the saturation
- B is the expansion factor of the oil when it is extracted from the reservoir and stored at ambient pressure.
- the porosity is spatially distributed based on its statistical distribution ⁇ , mea ns 0 f geo-statistical algorithm providing a predetermined longitudinal correlation equation and a predetermined orientation.
- the spatial distribution of the porosity is defined in each scenario of the plurality of scenarios.
- the permeability property (K) is determined for each scenario.
- the records of the analogues data base (2) have to comprise the permeability property of the materials and the porosity. From the selected records the correlation between the permeability versus the porosity is calculated wherein most of the cases the law between both variables is expressed as the loga- rithmic value of the permeability versus the porosity according to the Carman-Kozeny law.
- the selected records are those selected when the reference values ⁇ TM ⁇ , ⁇ TM ⁇ ⁇ ' , and ⁇ $> re f are obtained from the analogues data base (2).
- ⁇ 3 is used to fit the scatter from the relation between permeability (K) and porosity ( ⁇ ).
- the coefficient C can be estimated using a least square regression. To allow low scatter in the distribution further filters on the analogues results can be applied such as exploit possible correlation with additional variables as could be depth.
- the permeability variance has also been parametrized using a linear regression model as a function of porosity in order to correctly propagate the uncertainty in the three/dimensional realizations. To have a reliable simulation it is important to separate the effects of the mean (the trend) which is deterministic with porosity and the variance which is simulated by geo-statistical techniques.
- the correlation law provides the scalar distribution of the permeability as a function of - li the porosity.
- the geo- statistical algorithm By means of a geo statistical algorithm, for each domain of the generated scenarios the permeability as a scalar field is defined over said three-dimensional domain. For each scenario generated when the porosity has been populated, the geo- statistical algorithm generates a plurality of scenarios taking into account the variabil- ity of the permeability property.
- Figure 4 shows the initial scalar data, the first group of scenarios generated when populating the porosity property taking into account uncertainty; and, according to the last step, the plurality of scenarios departing from each former scenario wherein this second generation of scenarios provides the variability of the permeability taking into account how the uncertainty for said permeability proper- ty is propagated.
- the water saturation is calculated. To compute initial water saturation and later the net-to-gross, the results for analogous have been further exploit.
- the selected records those selected when the reference values ⁇ TM ⁇ , ⁇ TM ⁇ ⁇ ' , and 0 r ef are obtained from the analogues data base (2), also comprises the water saturation, or data on water saturation allowing to calculate the S for instance by means of correlations taking into account several properties different from the water saturation.
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Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP14733639.0A EP3014560A1 (en) | 2013-06-28 | 2014-06-27 | Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir |
BR112015032537A BR112015032537A2 (en) | 2013-06-28 | 2014-06-27 | method for generating hydrocarbon reservoir scenarios based on limited information in a target hydrocarbon reservoir |
CN201480036798.3A CN105453125A (en) | 2013-06-28 | 2014-06-27 | Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir |
CA2914465A CA2914465A1 (en) | 2013-06-28 | 2014-06-27 | Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir |
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
EP13382254.4A EP2819086A1 (en) | 2013-06-28 | 2013-06-28 | Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir |
EP13382254.4 | 2013-06-28 | ||
US14/220,397 US9458713B2 (en) | 2012-11-14 | 2014-03-20 | Generating hydrocarbon reservoir scenarios from limited target hydrocarbon reservoir information |
US14/220,397 | 2014-03-20 |
Publications (1)
Publication Number | Publication Date |
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WO2014207197A1 true WO2014207197A1 (en) | 2014-12-31 |
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Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
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PCT/EP2014/063689 WO2014207197A1 (en) | 2013-06-28 | 2014-06-27 | Method to generate scenarios of hydrocarbon reservoirs based on limited amount of information on a target hydrocarbon reservoir |
Country Status (5)
Country | Link |
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EP (1) | EP3014560A1 (en) |
CN (1) | CN105453125A (en) |
BR (1) | BR112015032537A2 (en) |
CA (1) | CA2914465A1 (en) |
WO (1) | WO2014207197A1 (en) |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009145960A1 (en) * | 2008-04-17 | 2009-12-03 | Exxonmobil Upstream Research Company | Robust optimization-based decision support tool for reservoir development planning |
US20110118983A1 (en) * | 2009-11-19 | 2011-05-19 | Chevron U.S.A. Inc. | System and method for reservoir analysis background |
Family Cites Families (4)
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US7922076B2 (en) * | 2006-07-27 | 2011-04-12 | Diebold Self-Service Systems | Banking apparatus operated responsive to data bearing records |
EP2480914B1 (en) * | 2009-09-25 | 2021-03-10 | Landmark Graphics Corporation | Systems and methods for the quantitative estimate of production-forecast uncertainty |
CN102834739B (en) * | 2010-12-17 | 2016-01-20 | 雪佛龙美国公司 | Estimate the system and method for the geologic structure of geologic body |
CN102426390B (en) * | 2011-10-21 | 2013-07-03 | 中国石油大学(北京) | Method for determining reserve volume of nonhomogeneous sandstone reservoir |
-
2014
- 2014-06-27 WO PCT/EP2014/063689 patent/WO2014207197A1/en active Application Filing
- 2014-06-27 BR BR112015032537A patent/BR112015032537A2/en active Search and Examination
- 2014-06-27 CN CN201480036798.3A patent/CN105453125A/en active Pending
- 2014-06-27 EP EP14733639.0A patent/EP3014560A1/en not_active Ceased
- 2014-06-27 CA CA2914465A patent/CA2914465A1/en not_active Abandoned
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2009145960A1 (en) * | 2008-04-17 | 2009-12-03 | Exxonmobil Upstream Research Company | Robust optimization-based decision support tool for reservoir development planning |
US20110118983A1 (en) * | 2009-11-19 | 2011-05-19 | Chevron U.S.A. Inc. | System and method for reservoir analysis background |
Non-Patent Citations (1)
Title |
---|
JEF CAERS ET AL: "Multiple-point geostatistics: a quantitative vehicle for integrating geologic analogs into multiple reservoir models", 2 January 2002 (2002-01-02), XP055085091, Retrieved from the Internet <URL:http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.218.1571> [retrieved on 20131023] * |
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Publication number | Publication date |
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CN105453125A (en) | 2016-03-30 |
CA2914465A1 (en) | 2014-12-31 |
BR112015032537A2 (en) | 2017-07-25 |
EP3014560A1 (en) | 2016-05-04 |
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