CN113496301B - Oil and gas field asset evaluation method and device - Google Patents
Oil and gas field asset evaluation method and device Download PDFInfo
- Publication number
- CN113496301B CN113496301B CN202010249090.3A CN202010249090A CN113496301B CN 113496301 B CN113496301 B CN 113496301B CN 202010249090 A CN202010249090 A CN 202010249090A CN 113496301 B CN113496301 B CN 113496301B
- Authority
- CN
- China
- Prior art keywords
- data
- yield
- oil
- production
- recovery
- 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.)
- Active
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 64
- 238000004519 manufacturing process Methods 0.000 claims abstract description 188
- 238000011084 recovery Methods 0.000 claims abstract description 140
- 238000000034 method Methods 0.000 claims abstract description 59
- 238000003860 storage Methods 0.000 claims abstract description 51
- 230000035699 permeability Effects 0.000 claims abstract description 50
- 238000005457 optimization Methods 0.000 claims abstract description 32
- 238000000605 extraction Methods 0.000 claims abstract description 15
- 230000003247 decreasing effect Effects 0.000 claims description 29
- 230000007423 decrease Effects 0.000 claims description 26
- 238000004590 computer program Methods 0.000 claims description 13
- 230000000638 stimulation Effects 0.000 claims description 5
- 230000003467 diminishing effect Effects 0.000 claims description 2
- 239000002699 waste material Substances 0.000 claims description 2
- 239000007789 gas Substances 0.000 abstract description 50
- 238000011161 development Methods 0.000 abstract description 14
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 abstract description 4
- 239000003345 natural gas Substances 0.000 abstract description 2
- 239000003209 petroleum derivative Substances 0.000 abstract description 2
- 239000003921 oil Substances 0.000 description 161
- 238000010586 diagram Methods 0.000 description 17
- 230000000750 progressive effect Effects 0.000 description 11
- 239000010410 layer Substances 0.000 description 9
- 230000008901 benefit Effects 0.000 description 7
- BVKZGUZCCUSVTD-UHFFFAOYSA-L Carbonate Chemical compound [O-]C([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-L 0.000 description 5
- 238000010276 construction Methods 0.000 description 5
- 239000010779 crude oil Substances 0.000 description 5
- 230000009467 reduction Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 238000002347 injection Methods 0.000 description 3
- 239000007924 injection Substances 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 238000011234 economic evaluation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000003129 oil well Substances 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000005526 G1 to G0 transition Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000009933 burial Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 150000004649 carbonic acid derivatives Chemical class 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 239000011229 interlayer Substances 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The invention provides an oil and gas field asset evaluation method and device, and relates to the technical field of petroleum and natural gas exploration and development, wherein the method comprises the following steps: acquiring basic parameters of an oil-gas field and evaluation parameters of a bid-in contract; determining a peak production and stable production prediction model according to the recovery ratio data, the recovery degree data, the storage ratio data and the geological storage data; determining a prediction model of yield and workload according to the storage ratio data, the oil extraction speed data, the yield data in the descending stage, the yield data in the increasing production and the single well productivity data; determining a well group optimization prediction model according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data; and generating an oil and gas field asset evaluation result according to the peak yield and peak stable yield prediction model, the yield and workload prediction model and the well group optimization prediction model. The invention optimizes the prediction speed and obtains more accurate prediction results based on limited parameters.
Description
Technical Field
The invention relates to the technical field of petroleum and natural gas exploration and development, in particular to an oil and gas field asset evaluation method and device.
Background
The focus of oil and gas field asset technology evaluation is to determine medium and long term production and workload profiles. The existing medium-long term planning and programming method mainly comprises the following steps: numerical simulation methods, generalized prediction model methods, 5-year yield composition methods, and storage ratio dynamic control prediction methods. For some situations, for example, in the purchase of new overseas projects, a medium-long term evaluation scheme needs to be completed according to a contract term, and the problems of less available materials, short evaluation time and the like are generally faced, so that it is extremely difficult or even impossible to obtain a relatively accurate evaluation result in a short time through the existing medium-long term planning and compiling method.
Disclosure of Invention
The invention provides an oil and gas field asset evaluation method and device, which can improve the prediction speed and accuracy of an oil and gas field asset evaluation result.
In a first aspect, an embodiment of the present invention provides a method for evaluating an oil and gas field asset, the method comprising: acquiring basic parameters of an oil-gas field and evaluation parameters of a bid-in contract; the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-closing evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a yield increasing stage, single well productivity data and well spacing data; determining a peak production and peak stable production prediction model from the recovery data, the production level data, the reserve ratio data, and the geological reserve data; determining a predictive model of production and workload based on the production ratio data, the production rate data, the run-down stage production data, the stimulation production data, and the single well production data; determining a well group optimization prediction model according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data; generating an oil and gas field asset evaluation result according to the peak yield and peak stable yield prediction model, the yield and workload prediction model and the well group optimization prediction model; the evaluation result at least comprises yield profile data and workload data corresponding to the yield profile data;
Determining the following formulas from the recovery data, the production level data, the reserve ratio data, and the geological reserve data to obtain a peak production and peak stable production prediction model:
PPTR＝(URF-RF1)÷(RP+T)
PPT＝OOIP*PPTR÷365
wherein, PPT is peak output, PPTR is oil extraction speed of geological reserves in peak, URF is final recovery of oil field, RF1 is the recovery degree before peak, RP is the recovery ratio, T is stable output time in peak, OOIP is geological reserves;
the oil recovery rate data includes a waste time geologic reservoir oil recovery rate and a diminishing initial time geologic reservoir oil recovery rate, the method comprising:
determining the following formula according to the storage ratio data, the oil extraction speed data, the yield data in the decreasing stage, the yield data in the increasing production and the single well productivity data to obtain a prediction model of yield and workload:
workload= (q×d+Δq)/single well capacity
Wherein D is i For decreasing the instantaneous rate of decrease at the beginning, Q is the yield at the decreasing stage, M is the recovery ratio calculated based on the recovery ratio, ve is the geological reserve recovery rate at the time of abandonment, vi is the geological reserve recovery rate at the beginning of decrease, D is the rate of decrease at a certain period, and DeltaQ is the yield increase data;
Determining the following formula according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data to obtain a well group optimization prediction model:
wherein E is R For recovery, L is the well spacing, H is the effective thickness of the oil reservoir, K h For horizontal permeability, K v Is vertical permeability.
In a second aspect, an embodiment of the present invention further provides an oil and gas field asset evaluation device, where the device includes: the acquisition module is used for acquiring the basic parameters of the oil and gas field and the bid-signing contract evaluation parameters; the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-closing evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a yield increasing stage, single well productivity data and well spacing data; a first determination module for determining a peak production and peak stable production prediction model based on the recovery data, the production level data, the reserve data, and the geological reserve data; the second determining module is used for determining a prediction model of yield and workload according to the storage ratio data, the oil extraction speed data, the yield data in the decreasing stage, the yield data in the increasing stage and the single well productivity data; the third determining module is used for determining a well group optimization prediction model according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data; the evaluation module is used for generating an oil and gas field asset evaluation result according to the peak yield and peak stable yield prediction model, the yield and workload prediction model and the well group optimization prediction model; the evaluation result at least comprises yield profile data and workload data corresponding to the yield profile data;
The first determining module is specifically configured to:
determining the following formulas from the recovery data, the production level data, the reserve ratio data, and the geological reserve data to obtain a peak production and peak stable production prediction model:
PPTR＝(URF-RF1)÷(RP+T)
PPT＝OOIP*PPTR÷365
wherein, PPT is peak output, PPTR is oil extraction speed of geological reserves in peak, URF is final recovery of oil field, RF1 is the recovery degree before peak, RP is the recovery ratio, T is stable output time in peak, OOIP is geological reserves;
the second determining module is specifically configured to:
determining the following formula according to the storage ratio data, the oil extraction speed data, the yield data in the decreasing stage, the yield data in the increasing production and the single well productivity data to obtain a prediction model of yield and workload:
workload= (q×d+Δq)/single well capacity
Wherein D is i For decreasing the instantaneous rate of decrease at the beginning, Q is the yield at the decreasing stage, M is the recovery ratio calculated based on the recovery ratio, ve is the geological reserve recovery rate at the time of abandonment, vi is the geological reserve recovery rate at the beginning of decrease, D is the rate of decrease at a certain period, and DeltaQ is the yield increase data;
the third determining module is specifically configured to:
Determining the following formula according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data to obtain a well group optimization prediction model:
wherein E is R For recovery, L is the well spacing, H is the effective thickness of the oil reservoir, K h For horizontal permeability, K v Is vertical permeability.
In a third aspect, an embodiment of the present invention further provides a computer device, including a memory, and a processor, where the memory stores a computer program that can run on the processor, and when the processor executes the computer program, the method for evaluating an oil and gas field asset is implemented.
In a fourth aspect, embodiments of the present invention also provide a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the above-described method of evaluating an oil and gas field asset.
The embodiment of the invention has the following beneficial effects: the embodiment of the invention provides an oil and gas field asset evaluation scheme, which comprises the steps of acquiring basic data required by oil and gas field asset technical evaluation by acquiring oil and gas field basic parameters and bid contract evaluation parameters, wherein the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-accepting contract evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a single well, single well productivity data and well spacing data; and then determining a peak production and peak stable production prediction model according to the recovery ratio data, the production degree data, the storage ratio data and the geological storage data, determining a prediction model of production and workload according to the storage ratio data, the production speed data, the yield data in the descending stage, the yield data in the increasing stage and the single well production data, determining a well group optimization prediction model according to the recovery ratio data, the oil deposit effective thickness data, the well spacing data and the permeability data, and finally generating an oil and gas field asset evaluation result by using the determined peak production and peak stable production prediction model, the determined yield and workload prediction model and the well group optimization prediction model, wherein the evaluation result at least comprises the production profile data and the workload data corresponding to the production profile data. The embodiment of the invention has less parameters, optimizes the prediction speed, obtains more accurate prediction results based on limited parameters, and can meet the requirement of completing bidding research in a short time.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an oil and gas field asset evaluation method provided by an embodiment of the invention;
FIG. 2 is a schematic illustration of a well group model provided in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a preferred well spacing of a CO2 WAG production method under different reservoir conditions according to an embodiment of the present invention;
FIG. 4 is a cross-sectional view of the A oilfield recommended production provided by the embodiment of the invention;
FIG. 5 is a new well plan view of an A oilfield recommendation provided in an embodiment of the present invention;
FIG. 6 is a cross-sectional view of a B-field production run provided by an embodiment of the present invention;
FIG. 7 is a plan view of a new well production in field B provided by an embodiment of the present invention;
FIG. 8 is a flow chart of a theoretical predictive model method for medium-long term planning in an oil and gas field, provided by an embodiment of the invention;
FIG. 9 is a statistical chart of the final storage ratio of the stable production period of the oil fields at home and abroad, which is provided by the embodiment of the invention;
FIG. 10 is a schematic diagram of a relationship between daily oil production and cumulative oil production in an oilfield according to an embodiment of the present invention;
FIG. 11 is a block diagram of an oil and gas field asset evaluation device according to an embodiment of the present invention;
fig. 12 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Overseas oil and gas field asset technology assessment is substantially similar to domestic medium-to-long term planning. The core content of the domestic medium-and-long-term planning is to predict the yield profile and the corresponding workload in the contract period, so as to optimize reasonable scheme indexes. Because of the similarity and difference between the geological features of oil reservoirs and the production dynamic features of the oil fields and the inconsistent production time period, the prediction method and the principle used have the similarity and the characteristics. The existing medium-long term planning and programming method mainly comprises the following steps:
(1) Numerical simulation method
If the production time is longer, the data are more sufficient and complete, a geological model which can reflect the oil field structural form, the reservoir characteristics and the fluid seepage law can be established, and on the basis, the simulation prediction is carried out by using the full oil field oil reservoir digital model. When the production time is short or the data is incomplete and is not systematic, the whole oil field numerical simulation method is difficult to predict the yield profile and the workload.
(2) Generalized predictive model method
According to research results in the fields of life systems, economic growth systems and the like, prediction is performed by establishing a differential form mathematical model of an oil and gas field yield generalized prediction model and a generalized prediction model of accumulated yield of the oil and gas field. The models are accurate in predicting the oil field output profile entering the high-water-content development period, cannot predict the workload, have short development period or are not put into development, and are difficult to predict. Usually no longer in overseas new project evaluation
(3) Method for forming 5 year yield
The 5-year yield formation method is based on trend prediction (Arps model method), and is matched with the matching relation of related development indexes, and the development wells are divided into units according to the production time by taking 5 years as basic units, so that the yield predictions are respectively carried out, and the yield indexes in the prediction period are formed by superposition. The method is mainly used for predicting the output of the medium-long-term oilfield in China.
(4) Dynamic control prediction method for storage ratio
The method is a method for predicting the yield by using the ratio of the residual recoverable reserves as a control condition on the basis of predicting the newly increased recoverable reserves in the prediction period. The basic idea of the calculation is as follows: after the oil field enters the development relative maturity period, the change rule of the storage ratio is researched, and the change rule is used as a control condition to predict the yield of the residual life cycle. The method is also mainly applied to the predicted production profile of the mature oil field.
Based on the method and the device, the actual oil field experience is combined with the theoretical formula principle by applying the novel combination method of the prediction model and the well group model, and a set of fast, accurate and effective theoretical prediction model method for medium-long term planning is formed on the basis of inheritance of former achievements, so that the method and the device not only have inheritance but also have originality. Meanwhile, by tightly combining different requirements of the contract, the comparison and optimization of various schemes are adopted to predict the reasonable yield profile and the corresponding workload according with the reality, thereby not only meeting the contract requirements of overseas new project evaluation, but also reducing the development risk and obtaining the economic benefit to the maximum extent.
For the sake of understanding the present embodiment, first, a detailed description will be given of a method and apparatus for evaluating an oil and gas field asset disclosed in the embodiments of the present invention.
The embodiment of the invention provides an oil and gas field asset evaluation method, which is shown in a flow chart of the oil and gas field asset evaluation method in fig. 1, and comprises the following steps:
and step S102, acquiring the basic parameters of the oil and gas field and the evaluation parameters of the bid-in contract.
In the embodiment of the invention, the basic parameters of the oil and gas field refer to one or a combination of any of stratum parameters, fluid parameters, testing and production parameters, for example, the parameters comprise at least geological reserves data, effective thickness data of oil reservoirs and permeability data, and the parameters for evaluation of bid-bearing contracts refer to related parameters such as technical services, product components and tax, for example, the parameters comprise at least recovery ratio data, recovery degree data, recovery ratio data, oil production speed data, yield data of a decrementing stage, yield increase data, single well yield data, well spacing data and the like.
It should be noted that, the permeability data may be used to determine whether the quality of the oil reservoir is good, the high permeability indicates good fluidity, the productivity may be relatively high, and the permeability data may be obtained through logging data, core data, and the like.
And step S104, determining a peak yield and stable production period prediction model according to the recovery ratio data, the recovery degree data, the storage ratio data and the geological storage data.
In embodiments of the invention, the recovery data may be pre-processed, for example, by model prediction. The data of the extraction degree includes the extraction degree before the peak period, and the value of the storage ratio data can be set according to the experience value or the actual requirement, which is not particularly limited in the embodiment of the invention. The peak and stable yields prediction model is used to describe the correlation between peak and stable yields, and after obtaining recovery data, production level data, storage ratio data, and geological storage data, the peak and stable yields may be determined based on the desired peak yields, or the peak yields may be determined based on the estimated peak and stable yields.
The peak stable period refers to the period during which the peak output can last.
And S106, determining a prediction model of the yield and the workload according to the storage ratio data, the oil extraction speed data, the yield data in the descending stage, the yield data in the increasing production and the single well productivity data.
In an embodiment of the present invention, a prediction model of yield and workload may be used to describe the correlation between yield and workload in the decrementing stage. After obtaining the production ratio data, the production rate data, the decrement phase production data, the stimulation production data, and the single well production data, a value of the workload may be determined based on a predictive model of the production and the workload.
The value of the recovery ratio data used in step S106 may be calculated after the recovery ratio is calibrated.
And S108, determining a well group optimization prediction model according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data.
In the embodiment of the invention, the well group optimization prediction model is used for describing the association relationship among the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data, and after the recovery ratio data, the oil reservoir effective thickness data and the permeability data are obtained, the reasonable well spacing can be obtained according to the well group optimization prediction model, or after the oil reservoir effective thickness data, the well spacing data and the permeability data are obtained, the recovery ratio data can be obtained.
And step S110, generating an oil and gas field asset evaluation result according to the peak yield and peak stable yield prediction model, the yield and workload prediction model and the well group optimization prediction model.
In the embodiment of the invention, the prediction model of the peak output and the steady output of the peak, the prediction model of the output and the workload and the optimization prediction model of the well group can obtain the output and workload data of different stages, and the output data and the workload data of each stage are processed to obtain at least the output profile data and the workload data corresponding to the output profile data.
The embodiment of the invention provides an oil and gas field asset evaluation scheme, which comprises the steps of acquiring basic data required by oil and gas field asset technical evaluation by acquiring oil and gas field basic parameters and bid contract evaluation parameters, wherein the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-accepting contract evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a single well, single well productivity data and well spacing data; and then determining a peak production and peak stable production prediction model according to the recovery ratio data, the production degree data, the storage ratio data and the geological storage data, determining a prediction model of production and workload according to the storage ratio data, the production speed data, the yield data in the descending stage, the yield data in the increasing stage and the single well production data, determining a well group optimization prediction model according to the recovery ratio data, the oil deposit effective thickness data, the well spacing data and the permeability data, and finally generating an oil and gas field asset evaluation result by using the determined peak production and peak stable production prediction model, the determined yield and workload prediction model and the well group optimization prediction model, wherein the evaluation result at least comprises the production profile data and the workload data corresponding to the production profile data. The embodiment of the invention has less parameters, optimizes the prediction speed, obtains more accurate prediction results based on limited parameters, and can meet the requirement of completing bidding research in a short time.
In order to minimize the use of parameters for faster prediction of field asset assessment results, determining peak production and peak stable production prediction models from recovery data, production level data, production ratio data, and geological reserve data may be performed as follows:
determining a first correlation between recovery ratio data, production degree data, storage ratio data, steady production period at peak time and recovery rate of geological reserves at peak time; determining a second correlation between the production rate of the peak geological reserve and the geological reserve data and the peak production; and taking the first association relation and the second association relation as a peak output and peak stable output period prediction model.
In the embodiment of the invention, after the recovery ratio data, the recovery degree data, the recovery ratio data and the geological reserve data are obtained, the oil recovery speed of the geological reserve in the peak period can be determined according to the required high peak period yield, the peak period stable yield period can be calculated based on the oil recovery speed of the geological reserve in the peak period, or the oil recovery speed of the geological reserve in the peak period can be determined based on the estimated peak period stable yield period, and the peak period yield can be determined based on the oil recovery speed of the geological reserve in the peak period.
In order to improve the accuracy of the prediction, the following steps may be performed:
determining the following formulas according to the recovery ratio data, the recovery degree data, the recovery ratio data and the geological reserve data to obtain a peak yield and peak stable yield prediction model: pptr= (URF-RF 1)/(rp+t), ppt=ooip × PPTR +.365, where PPT is peak production, PPTR is the rate of recovery of the geological reserve at peak, URF is the final recovery of the field, RF1 is the extent of recovery before peak, RP is the recovery ratio, T is the length of stable production at peak, OOIP is the geological reserve.
In the present example, PPT-peak yield, MB/D; pptr—oil recovery rate of peak geological reserves,%; URF-oil field final recovery,%; RF 1-the extent of production of the build before peak time,%; RP-storage ratio, dimensionless; t is the stable yield time of peak period, and the year; OOIP-geological reserves, MMB.
In order to minimize the use of parameters for faster prediction of field asset assessment results, determining a yield and workload prediction model based on the production ratio data, the production rate data, the run-down phase yield data, the production yield data, and the individual well capacity data may be performed as follows:
Determining a third association between the production ratio data, the production speed data and the rate of decline; determining a fourth association between the workload, the yield data at the decreasing stage, the yield data at the increasing and decreasing rate and the single well productivity data; and taking the third association relation and the fourth association relation as a prediction model of the fixed yield and the workload.
In the embodiment of the invention, the more accurate reduction rate can be obtained by researching the change condition of the reduction rate along with time. And then, the workload is predicted based on the reduction rate, so that the randomness of the evaluator in using the reduction rate can be overcome, and the accuracy of a prediction result is improved.
In order to improve the accuracy of the prediction, the following steps may be performed: collectingThe oil velocity data comprises a geological reserve oil recovery velocity during abandonment and a geological reserve oil recovery velocity during initial decrement, and the following formulas are determined according to the reserve ratio data, the oil recovery velocity data, the yield data during decrement, the yield data during yield increase and the single well yield data so as to obtain a prediction model of yield and workload:workload= (q×d+Δq)/single well capacity, where Di is the instantaneous rate of decline at the beginning of decline, Q is the yield at the decline stage, M is the recovery ratio calculated based on recovery, ve is the geological reserve recovery rate at disposal, vi is the geological reserve recovery rate at the beginning of decline, D is the decline rate at a certain period, Δq is the yield increase data.
In the embodiment of the invention, a cumulative yield theoretical formula model of a decrementing stage of a put-into-production oilfield is firstly applied:wherein: np—cumulative yield at decremental stage, MMB; di-instantaneous rate of decrease at the beginning of decrease, decimal; qi-decreasing yield at initiation, MB/d; n-decreasing exponent, decimal; q-yield of the decrementing stage, MB/d. The yield decrementing types are three of exponential decrementing, hyperbolic decrementing and harmonic decrementing, the most common decrementing in practice being hyperbolic decrementing, and the most common in hyperbolic decrementing is the well-known carbotoff formula: that is, the hyperbolic decrease when n=0.5 is decreased, most oil fields are continuously advanced along with development process, the decreasing rule approaches to the hyperbolic decrease when n=0.5, and the following relation model can be established: />Establishing a relation between workload and yield and single well productivity: workload= (q×d+Δq)/single well capacity, where: m-storage ratio, dimensionless; ve-geological reserve oil recovery rate when abandoned,%; vi—decreasing the initial geological reserve oil recovery rate,%; d-decreasing rate, decimal; deltaQ-yield increase, MB/d.
In order to minimize the use of parameters to facilitate faster prediction of field asset evaluation results, determining a well group optimization prediction model based on recovery data, reservoir effective thickness data, well spacing data, and permeability data may be performed as follows:
Determining fifth association relations among recovery ratio data, oil reservoir effective thickness data, well spacing data and permeability data; and taking the fifth association relation as a well group optimization prediction model.
In the embodiment of the invention, a corresponding typical well group model is established, and the longitudinal heterogeneity is accurately described by referring to a well group model schematic diagram shown in fig. 2, and on the basis, the oil well productivity, a reasonable flooding pattern and well spacing are determined.
In order to improve the accuracy of the prediction, the following steps may be performed:
determining the following formula according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data to obtain a well group optimization prediction model:wherein E is R For recovery, L is the well spacing, H is the effective thickness of the oil reservoir, K h For horizontal permeability, K v Is vertical permeability.
In the embodiment of the invention, ER- - -recovery ratio,%; l- - -well spacing, m; h, the effective thickness of the oil reservoir, m; kh- - -horizontal permeability, mD; kv/Kh- -ratio of vertical permeability to horizontal permeability.
It should be noted that, referring to a schematic diagram of well spacing optimization of CO2 WAG production mode under different oil reservoir conditions shown in fig. 3, under the condition of recovery calibration, a reasonable well spacing can be calculated reversely. The workload is determined, namely the number of production wells of the oil-gas field or the oil-gas reservoir is determined, the number of injection wells is determined through the injection well pattern, and the total number of development wells is the sum of the two.
In order to ensure stable production of the oil field, the method can further comprise the following steps:
the value of the storage ratio data is a numerical value between 9 and 12.
A large number of oil field development practices show that the storage ratio of the stable production period of the oil field is controlled between 9 and 12. It should be noted that the higher the storage ratio is, the easier the oil field is to stabilize production; otherwise, the oil field is difficult to stably produce; the lowest production ratio of the stable production period of the oil field can be differentiated according to different oil fields, and the production ratio of the stable production end period set for the bidding oil field can be controlled to be 3 levels, namely 10, 11 and 12.
The present embodiment will be described in detail with reference to examples.
(1) Actual oilfield data verification
To verify the accuracy and reliability of the model, four example fields overseas were used for verification. The production time of the four oil fields is approximately ten years at the end of 2008, and the four oil fields are all oil fields for multiple oil field adjustment schemes. The results show that: the theoretical predicted production well number is very close to the actual production number, and the error is within 10 percent, which is acceptable (see
Table 1).
Table 1 example oilfield production conditions and theoretical prediction results comparison table
Accurate predictions of oilfield production and corresponding workload are a difficulty in bidding both the first and second rounds of country C. By communicating with a plurality of international partner companies, two main methods are found for predicting the production profile and the corresponding workload, namely, the first method adopts digital-analog prediction or single well group digital-analog prediction, the second method adopts conventional oilfield progressive rate to predict the production profile, and then adopts single well production progressive rate to predict the workload. Although the digital-analog prediction is complete in theory, the digital-analog prediction needs more parameters and has large workload, is often too ideal, and is difficult to meet the requirement of C-country bidding with very short research time. The second method is that the oilfield progressive reduction rate is given completely by experience, the single well production progressive profile is formulated by experience, the single well production progressive profile of different well patterns is actually different, and the single well production progressive profile of different time production wells is also different, so that the method has great randomness.
The accuracy of the method mainly depends on the recovery ratio and the single well productivity prediction reliability, the progressive rate is only related to the oil field storage ratio and changes along with the change of the storage ratio, the hyperbolic progressive rate is considered to be more in accordance with the practice of long-term exploitation of the oil reservoir, the single well progressive rate is not considered in the work load prediction, and the single well progressive rate is only hidden in the full oil field progressive rate. The theoretical prediction method is brand new and is not applied to other international large petroleum companies at present. The method considers that the decrease of the oil field exists from the initial production stage of the oil field, but the decrease rate is very low, so that the problem that the decrease method for a plurality of companies is difficult to predict the workload of stable production of the oil field is solved. The stationary phase of the oil field is relative, e.g., the oil field is stable; the stable production of the oil well is maintained by amplifying the pressure difference, if the production flow pressure at the bottom of the well is kept unchanged, according to unstable well test, the well yield is increased due to pressure propagation, the resistance is increased, and the yield is actually reduced. The method mainly predicts production wells, and the number of water injection wells is mainly given according to the balance of oil field production.
(2) A oil field medium-long term fast prediction scheme
The oil field A is characterized in that the long axis of the oil field A is anticline from north to south and is about 80km long and 10-14km wide, the two construction high points of south and north are separated, the area is 1769.6km2, and the depth of the oil reservoir is 2300-3500 m. The target layer was a chalky 4-layer oil layer: z three sets of sandstone and M carbonate. The reservoir is stable in distribution and good in continuity. Z sandstone is thick-layer sandstone and thin-layer mudstone, the thickness is about 130 meters, the thickness of pure sandstone is 100 meters, the average porosity is 17%, and the permeability is 430mD, and the separation is good. M is blocky carbonate rock with lamellar characteristics, the thickness is about 130M, the internal interlayer and hypertonic zone develop, the average porosity is 13%, and the permeability is 55mD. The crude oil of the Z sandstone reservoir has good properties, and the crude oil has a weight of 28-35API; m carbonate reservoir crude oil properties are relatively poor, crude oil weight 24-28API. The recovery ratio of Z estimated by sandstone of the oil field A is 55% -60% -62%, and the recovery ratio of estimated carbonate is 35% -40% -45%.
Table 2 can be obtained from the predictive model of peak yield and peak stable yield; determining the bidding range to be 2400MB/d-2800MB/d according to the table; the index basically corresponds to the range of medium and high recovery ratio; the stable period may be 4 to 7 years; under the condition of the storage ratio of 10 percent, the production can be stably carried out for at least more than 4 years under the condition of medium and high recovery ratio.
Prior to bidding, it was predicted that a company might bid at a peak yield of 3100MB/d based on the following table, and this scale was consistent with the scale of the past design of the field, that a company bid at 3125MB/d peak PPT and a return of $ 4 per barrel and was declared to bid. Unfortunately, however, the payoff per barrel given by the signer is $ 2 at maximum, and since peak production is 3125MB/d too high, the risk is relatively high even 4 years to steady production relative to my estimated maximum recovery range and lower storage ratio, thus incurring penalties that do not realize production at payoff per barrel of $ 2 at maximum, and economic benefit is severely impacted; a company eventually discards the bid and eventually my bid with a peak yield of 285 ten thousand barrels, although the bid price per barrel of my bid is $ 3.99, the peak yield of 285 ten thousand barrels can be steadily produced for 4 to 7 years, even 7 years, and the benefit of my is objective and bid is achieved under the condition that the bid price per barrel is $ 2 at maximum. The prediction model of the peak yield and the stable yield period of the peak period establishes a foundation for successful winning of the A oil field.
The storage ratio at the end of the stable production period of the prediction scheme is controlled to be more than 10, and the number of the encrypted wells and a new drilling plan before the end of the stable production period are determined by combining reasonable well spacing and single well productivity analysis. And 1288 new wells are drilled together in the same period of the prediction scheme, the production is stable for 4 years, the oil extraction speed in the peak period is lower than 2%, the final storage ratio in the production is higher than 11, and the prediction scheme is shown in a table 3, an A oil field recommended scheme yield profile diagram shown in fig. 4 and an A oil field recommended scheme new well planning diagram shown in fig. 5.
TABLE 2A peak oilfield yield and stationary production years
The peak stable production period of the prediction scheme is 4 years, which is reduced by 3 years compared with the 7-year stable production period required by the contract, the characteristics of the bidding contract are mainly considered, the too low yield target is determined by the determined bidding strategy and is difficult to bid, but the yield target of the prediction scheme is also robust, only we need to accept the penalty of being unable to stably produce for 7 years, if the economic evaluation is subject to the penalty of being unable to stably produce for 7 years, higher income of us can be obtained, and the prediction scheme can be used as a bidding scheme of us. The predictive protocol is very close to the actual winning protocol ppt=285 vans/day, with a work capacity of 1256 new wells.
Table 3A oilfield recommendation development index
(3) B oil field medium-long term fast prediction scheme
The B oil field is a gentle long-axis anticline of North west-south east trend, and the area of the same area is 288km 2 About 30km long and about 10km wide. Third line-chalk line total 9 sets of oil layer: two sets of sandstones, 7 sets of carbonates, and a burial depth of 1900-4600m. The thickness of the oil layer is 4.7-72m, the porous carbonate rock is taken as the main material, the porosity is 10-22%, and the permeability is 4-66mD. The main oil layer pressure is 4600-5200psi, the pressure coefficient is 1.12, the ground saturation pressure is about 2000psi, the crude oil weight is 19.5-40API, the average 21.6API, the viscosity is 1.3-3.1cp, and the H2S content is 0.09-0.5%.
According to the characteristics and reserves of geological oil reservoirs of oil fields, PPT=300, 400, 500, 600, 700, 800 thousand barrels/day construction period 7 years and slow construction (construction period 9 years) schemes are respectively designed, the development mode of succession of the layer is considered, the ground engineering is matched, contract terms are combined, investment is optimized, and after economic evaluation, the scheme is finally predicted: 9 years of construction period, ppt=600 MB/D; the actual winning bid scheme is as follows: ppt=535 MB/D (table 4, B field production run profile shown in fig. 6, and B field new well plan shown in fig. 7).
Table 4B oilfield bidding protocol-peak yield 60 thousand barrels per day
The embodiment of the invention provides an oil and gas field asset evaluation method and device, referring to a theoretical prediction model method flow chart of oil and gas field medium-long term planning shown in fig. 8, a domestic and foreign oil field steady production period end-of-production ratio statistical chart shown in fig. 9 and a relation curve diagram of daily oil production and accumulated oil production in a certain oil field shown in fig. 10. The theoretical prediction model has a great breakthrough in oil reservoir engineering theory and practice, and meets the requirement of completing bidding research in a short time. If the storage ratio of the stable production period of the oil field is controlled to be more than 10, and the combination of reasonable well spacing and single well productivity analysis is adopted, the number of new wells and reasonable production arrangement are determined, and the method can be used for predicting scheme indexes and workload. The method also provides a set of rapid evaluation method for the evaluation of oil and gas field projects such as overseas secondary oil recovery, secondary oil recovery and tertiary oil recovery, and the like, and improves the accuracy of yield profile and workload prediction.
The embodiment of the invention also provides an oil and gas field asset evaluation device, referring to the structural block diagram of the oil and gas field asset evaluation device shown in fig. 11, the device comprises:
an acquisition module 71 for acquiring the basic parameters of the oil and gas field and the evaluation parameters of the bid-in contract; the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-closing evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a yield increasing stage, single well productivity data and well spacing data; a first determination module 72 for determining peak production and peak stable production prediction models based on recovery data, production level data, storage ratio data, and geological storage data; a second determining module 73 for determining a predictive model of production and workload based on the production ratio data, the production rate data, the decreasing stage production data, the stimulation production data, and the individual well production data; a third determination module 74 for determining a well group optimization prediction model based on the recovery data, the reservoir effective thickness data, the well spacing data, and the permeability data; an evaluation module 75 for generating an oil and gas field asset evaluation result based on the peak production and peak stable production prediction model, the production and workload prediction model, and the well group optimization prediction model; the evaluation result at least includes the yield profile data and the workload data corresponding to the yield profile data.
In one embodiment, the first determining module is specifically configured to: determining a first correlation between recovery ratio data, production degree data, storage ratio data, steady production period at peak time and recovery rate of geological reserves at peak time; determining a second correlation between the production rate of the peak geological reserve and the geological reserve data and the peak production; and taking the first association relation and the second association relation as a peak output and peak stable output period prediction model.
In one embodiment, the first determining module is specifically configured to: determining the following formulas from the recovery data, the production level data, the reserve ratio data, and the geological reserve data to obtain a peak production and peak stable production prediction model: pptr= (URF-RF 1)/(rp+t), ppt=ooip × PPTR +.365, where PPT is peak production, PPTR is the rate of recovery of the geological reserve at peak, URF is the final recovery of the field, RF1 is the extent of recovery before peak, RP is the recovery ratio, T is the length of stable production at peak, OOIP is the geological reserve.
In one embodiment, the second determining module is specifically configured to: determining a third association between the production ratio data, the production speed data and the rate of decline; determining a fourth association between the workload, the yield data at the decreasing stage, the yield data at the increasing and decreasing rate and the single well productivity data; and taking the third association relation and the fourth association relation as a prediction model of the fixed yield and the workload.
In one embodiment, the second determining module is specifically configured to: based on the ratio data and the speed dataThe decreasing stage yield data, the stimulation yield data and the single well productivity data are formulated as follows to obtain a yield and workload prediction model:workload= (q×d+Δq)/single well capacity, where D i In order to decrement the instantaneous decrement rate at the initial time, Q is the yield of the decrement stage, M is the recovery ratio calculated based on the recovery ratio, ve is the geological reserve recovery speed at the time of abandonment, vi is the geological reserve recovery speed at the initial time of decrement, D is the decrement rate at a certain period, and DeltaQ is the yield increase data.
In one embodiment, the third determining module is specifically configured to: determining fifth association relations among recovery ratio data, oil reservoir effective thickness data, well spacing data and permeability data; and taking the fifth association relation as a well group optimization prediction model.
In one embodiment, the third determining module is specifically configured to: determining the following formula according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data to obtain a well group optimization prediction model:wherein E is R For recovery, L is the well spacing, H is the effective thickness of the oil reservoir, K h For horizontal permeability, K v Is vertical permeability. />
The embodiment of the present invention further provides a computer device, referring to the schematic block diagram of the structure of the computer device shown in fig. 12, where the computer device includes a memory 81 and a processor 82, and the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the processor implements the steps of any of the methods described above.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the computer device described above may refer to the corresponding process in the foregoing method embodiment, which is not repeated herein.
Embodiments of the present invention also provide a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the steps of any of the methods described above.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A method of evaluating an oil and gas field asset, comprising:
acquiring basic parameters of an oil-gas field and evaluation parameters of a bid-in contract; the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-closing evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a yield increasing stage, single well productivity data and well spacing data;
determining a peak production and peak stable production prediction model from the recovery data, the production level data, the reserve ratio data, and the geological reserve data;
determining a predictive model of production and workload based on the production ratio data, the production rate data, the run-down stage production data, the stimulation production data, and the single well production data;
determining a well group optimization prediction model according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data;
generating an oil and gas field asset evaluation result according to the peak yield and peak stable yield prediction model, the yield and workload prediction model and the well group optimization prediction model; the evaluation result at least comprises yield profile data and workload data corresponding to the yield profile data;
Determining the following formulas from the recovery data, the production level data, the reserve ratio data, and the geological reserve data to obtain a peak production and peak stable production prediction model:
PPTR＝(URF-RF1)÷(RP+T)
PPT＝OOIP*PPTR÷365
wherein, PPT is peak output, PPTR is oil extraction speed of geological reserves in peak, URF is final recovery of oil field, RF1 is the recovery degree before peak, RP is the recovery ratio, T is stable output time in peak, OOIP is geological reserves;
the oil recovery rate data includes a waste time geologic reservoir oil recovery rate and a diminishing initial time geologic reservoir oil recovery rate, the method comprising:
determining the following formula according to the storage ratio data, the oil extraction speed data, the yield data in the decreasing stage, the yield data in the increasing production and the single well productivity data to obtain a prediction model of yield and workload:
workload= (q×d+Δq)/single well capacity
Wherein D is i For decreasing the instantaneous rate of decrease at the beginning, Q is the yield at the decreasing stage, M is the recovery ratio calculated based on the recovery ratio, ve is the geological reserve recovery rate at the time of abandonment, vi is the geological reserve recovery rate at the beginning of decrease, D is the rate of decrease at a certain period, and DeltaQ is the yield increase data;
Determining the following formula according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data to obtain a well group optimization prediction model:
wherein E is R For recovery, L is the well spacing, H is the effective thickness of the oil reservoir, K h For horizontal permeability, K v Is vertical permeability.
2. The method of claim 1, wherein the stored ratio data has a value between 9 and 12.
3. An oil and gas field asset evaluation device, comprising:
the acquisition module is used for acquiring the basic parameters of the oil and gas field and the bid-signing contract evaluation parameters; the oil and gas field basic parameters at least comprise geological reserve data, oil reservoir effective thickness data and permeability data; the bid-closing evaluation parameters at least comprise recovery ratio data, production degree data, storage ratio data, oil production speed data, yield data in a decreasing stage, yield data in a yield increasing stage, single well productivity data and well spacing data;
a first determination module for determining a peak production and peak stable production prediction model based on the recovery data, the production level data, the reserve data, and the geological reserve data;
The second determining module is used for determining a prediction model of yield and workload according to the storage ratio data, the oil extraction speed data, the yield data in the decreasing stage, the yield data in the increasing stage and the single well productivity data;
the third determining module is used for determining a well group optimization prediction model according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data;
the evaluation module is used for generating an oil and gas field asset evaluation result according to the peak yield and peak stable yield prediction model, the yield and workload prediction model and the well group optimization prediction model; the evaluation result at least comprises yield profile data and workload data corresponding to the yield profile data;
the first determining module is specifically configured to:
determining the following formulas from the recovery data, the production level data, the reserve ratio data, and the geological reserve data to obtain a peak production and peak stable production prediction model:
PPTR＝(URF-RF1)÷(RP+T)
PPT＝OOIP*PPTR÷365
wherein, PPT is peak output, PPTR is oil extraction speed of geological reserves in peak, URF is final recovery of oil field, RF1 is the recovery degree before peak, RP is the recovery ratio, T is stable output time in peak, OOIP is geological reserves;
The second determining module is specifically configured to:
determining the following formula according to the storage ratio data, the oil extraction speed data, the yield data in the decreasing stage, the yield data in the increasing production and the single well productivity data to obtain a prediction model of yield and workload:
workload= (q×d+Δq)/single well capacity
Wherein D is i For decreasing the instantaneous rate of decrease at the beginning, Q is the yield at the decreasing stage, M is the recovery ratio calculated based on the recovery ratio, ve is the geological reserve recovery rate at the time of abandonment, vi is the geological reserve recovery rate at the beginning of decrease, D is the rate of decrease at a certain period, and DeltaQ is the yield increase data;
the third determining module is specifically configured to:
determining the following formula according to the recovery ratio data, the oil reservoir effective thickness data, the well spacing data and the permeability data to obtain a well group optimization prediction model:
wherein E is R For recovery, L is the well spacing, H is the effective thickness of the oil reservoir, K h For horizontal permeability, K v Is vertical permeability.
4. A computer device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor implements the steps of the method of claim 2 when the computer program is executed.
5. A computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of claim 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010249090.3A CN113496301B (en) | 2020-04-01 | 2020-04-01 | Oil and gas field asset evaluation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010249090.3A CN113496301B (en) | 2020-04-01 | 2020-04-01 | Oil and gas field asset evaluation method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113496301A CN113496301A (en) | 2021-10-12 |
| CN113496301B true CN113496301B (en) | 2024-03-29 |
Family
ID=77994204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010249090.3A Active CN113496301B (en) | 2020-04-01 | 2020-04-01 | Oil and gas field asset evaluation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113496301B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101096909A (en) * | 2006-06-30 | 2008-01-02 | 中国石油天然气股份有限公司勘探开发研究院 | Method for recognizing oil layer and evaluating production capacity |
| CN105631754A (en) * | 2015-12-29 | 2016-06-01 | 中国石油天然气股份有限公司 | Method and apparatus for determining output profile data of overseas oil field |
| KR101657890B1 (en) * | 2015-04-06 | 2016-09-20 | 서울대학교산학협력단 | Economic analysis of production rate of reservoir using multi-objective genetic algorithm and real option |
| CN107657365A (en) * | 2017-09-08 | 2018-02-02 | 中国石油天然气股份有限公司 | Geological resource extraction value evaluation method and device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130110524A1 (en) * | 2011-10-26 | 2013-05-02 | Nansen G. Saleri | Management of petroleum reservoir assets using reserves ranking analytics |
-
2020
- 2020-04-01 CN CN202010249090.3A patent/CN113496301B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101096909A (en) * | 2006-06-30 | 2008-01-02 | 中国石油天然气股份有限公司勘探开发研究院 | Method for recognizing oil layer and evaluating production capacity |
| KR101657890B1 (en) * | 2015-04-06 | 2016-09-20 | 서울대학교산학협력단 | Economic analysis of production rate of reservoir using multi-objective genetic algorithm and real option |
| CN105631754A (en) * | 2015-12-29 | 2016-06-01 | 中国石油天然气股份有限公司 | Method and apparatus for determining output profile data of overseas oil field |
| CN107657365A (en) * | 2017-09-08 | 2018-02-02 | 中国石油天然气股份有限公司 | Geological resource extraction value evaluation method and device |
Non-Patent Citations (10)
| Title |
|---|
| 唐80井区东部区域特低渗透裂缝性油藏井网方式及井距论证;薛金泉;程妮;;化学工程与装备(07);第80-82页 * |
| 孙宝刚 ; .葡北油田产量递减规律及极限储采比的确定.内蒙古石油化工.2011,(10),第240-242页. * |
| 开发指标变化规律在渤海油田产量及工作量预测中的应用;缪飞飞;;石油地质与工程(05);第66-70页 * |
| 张曙光 ; 于立君 ; 常毓文 ; 范海亮 ; .递减期油藏储采比变化规律研究.内蒙古石油化工.2010,(04),第104-107页. * |
| 油气田产量预测的模型研究;胡建国, 张栋杰, 陈元千;天然气工业(05);第31-34页 * |
| 缪飞飞 ; .开发指标变化规律在渤海油田产量及工作量预测中的应用.石油地质与工程.2017,(05),第66-70页. * |
| 胡建国,张栋杰,陈元千.油气田产量预测的模型研究.天然气工业.1997,(05),第31-34页. * |
| 葡北油田产量递减规律及极限储采比的确定;孙宝刚;;内蒙古石油化工(10);第240-242页 * |
| 薛金泉 ; 程妮 ; .唐80井区东部区域特低渗透裂缝性油藏井网方式及井距论证.化学工程与装备.2016,(07),第80-82页. * |
| 递减期油藏储采比变化规律研究;张曙光;于立君;常毓文;范海亮;;内蒙古石油化工(04);第104-107页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113496301A (en) | 2021-10-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102041995B (en) | System for monitoring complicated oil deposit flooding conditions | |
| CN104453804B (en) | Dynamic monitoring and evaluating method for gas-drive reservoir development | |
| CN110334431A (en) | A kind of low permeability tight gas reservoir single well controlled reserves calculating and remaining gas analysis method | |
| CN105134191B (en) | The evaluation method of fine and close oil well reserves | |
| CN104750896B (en) | A kind of fractured-cavernous carbonate reservoir method for numerical simulation | |
| CN104484556A (en) | Oilfield development evaluation method | |
| CN104879103A (en) | Layered water injection effect analysis method | |
| CN103912248A (en) | Method for predicting water contents of water-drive oilfields | |
| CN106150477A (en) | A kind of method determining single well controlled reserves | |
| CN107676064A (en) | A kind of water cut in water flooding reservoir Forecasting Methodology and its prediction meanss | |
| CN103472484A (en) | Horizontal well track optimization method based on RS three-dimensional sensitivity seismic attribution analysis | |
| CN111709847A (en) | Method for predicting recoverable reserves of top gas side water reservoir and evaluating development effect | |
| CN107944599A (en) | The Forecasting Methodology of oil gas horizontal well production | |
| CN105089566A (en) | Gas well system production allocation method | |
| CN107130955B (en) | Method for determining bottom hole flowing pressure and method for determining natural energy of reservoir | |
| CN104100263A (en) | Method for determining residual oil saturation of non-uniform water flooded layer | |
| CN115587674A (en) | Dynamic gas well capacity prediction method in expansion and yield reaching process of oil reservoir reconstruction gas storage | |
| CN106014365B (en) | A method of prediction waterflooding development Production Decline Prediction of Oilfield rate | |
| CN106481315A (en) | Land sandstone oil reservoir individual well recoverable reserves quickly determines model and method for building up | |
| CN113496301B (en) | Oil and gas field asset evaluation method and device | |
| Ali et al. | A semi-analytical method for history matching and improving geological models of layered reservoirs: CGM analytical method | |
| Li et al. | Potential evaluation of CO2 EOR and storage in oilfields of the Pearl River Mouth Basin, northern South China Sea | |
| CN115130268A (en) | Oil reservoir injection-production scheme design method and system, storage medium and electronic equipment | |
| CN114066166A (en) | Cltobalite low-permeability reservoir CO2Miscible flooding screening method | |
| Leemput et al. | Full-field reservoir modeling of Central Oman gas-condensate fields |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |