Method for optimizing resource usage for reservoir development
A computer-implemented method generates reservoir profiles and probability distributions to optimize resource use in oil reservoir development, addressing inefficiencies and safety issues in marginal reservoirs by selecting appropriate reservoirs for development.
Patent Information
- Application Number
- CN202280096274.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2022-04-19
- Publication Date
- 2024-12-31
AI Technical Summary
Developing marginal oil reservoirs leads to inefficient resource use, increased wear and tear of equipment, and unsafe operation due to frequent assembly and disassembly of oil delivery systems, with energy waste and material waste resulting from unplanned drilling processes.
A computer-implemented method for optimizing resource use by generating reservoir profiles based on input parameters, including probability distributions and sorting schemes, to select appropriate reservoirs for development, reducing the risk of inefficient resource use and improving safety.
The method enables efficient selection of reservoirs, minimizing resource waste, energy consumption, and equipment wear by accurately assessing reservoir potential, thereby optimizing resource utilization and ensuring safe operation.
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Figure CN119234081A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to a computer-implemented method, a data processing apparatus, and a computer program for optimizing the use of resources for developing an oil reservoir. Background Art
[0002] Typically, developing an oil reservoir is a complex process that includes placing and installing an oil delivery system (including devices such as drilling rigs, wells, pipeline systems, pipelines, tanks, control units, etc.). The normal operation of all devices of the oil delivery system is mandatory. Otherwise, in the event of a failure, damage may be caused to the devices of the oil delivery system itself, the environment, and / or the personnel operating the devices of the oil delivery system. When the devices of the oil delivery system are frequently assembled and disassembled, in addition to the common wear and tear of the devices caused by operation, the probability of failure may increase significantly. If a large oil reservoir including a large amount of recoverable oil is developed, typically, the assembly and disassembly of the devices required to develop the oil reservoir must be performed very rarely. Typically, the devices of the oil delivery system are set up and assembled once at the beginning of the development of the oil reservoir, and disassembled once when the development ends (for example, when the oil reservoir has run out of recoverable oil). Therefore, the devices of the oil delivery system used to develop the oil reservoir usually have to be set up / assembled and dismantled / disassembled only once.
[0003] Nowadays, many so-called marginal reservoirs are developed. A marginal reservoir is a reservoir comprising an unknown or at least difficult to predict amount of recoverable oil. In most cases, it can be determined with certainty that a reservoir has a sufficiently large amount of recoverable oil only after the reservoir has been fully developed, i.e. only after a complete oil delivery system with all its devices (such as drilling rigs, wells, etc.) has been installed and assembled. If it turns out that a marginal reservoir does not provide a sufficient amount of recoverable oil, the assembled oil delivery system including all its devices must be disassembled / removed within only a short period of time after it has been installed and assembled, so that the oil delivery system and its devices can be transported to another reservoir where they can be reused for development.
[0004] However, the frequent assembly, disassembly and transportation of the devices of the oil delivery system puts pressure on the devices of the oil delivery system. For example, the frequent loosening and tightening of bolted connections can cause above-average wear. In addition, the seals of the pipeline may become fragile, which significantly increases the risk of leakage. In addition, the load-bearing and supporting capacity of the supporting elements may deteriorate due to frequent assembly and disassembly, which may lead to the instability of the entire oil delivery system. Frequent assembly and disassembly for the purpose of moving the oil delivery system to a new reservoir may also increase the risk of transportation damage. Frequent assembly and disassembly may also increase the risk of incorrect assembly or disassembly of the parts of the oil delivery system due to human error. In addition, specific parts of the oil delivery system cannot be reused after they have been installed. For example, pipelines set up for transporting recoverable oil are usually welded together and therefore cannot be used for other reservoir development projects. Therefore, when it turns out that the recoverable oil provided by the marginal reservoir is insufficient, if used to develop another marginal reservoir, many devices, parts and components of the corresponding oil delivery system must be replaced or at least repaired to ensure safe operation, which leads to a waste of resources in terms of materials and tools required for repairing, maintaining and / or replacing the devices of the oil delivery system.
[0005] Furthermore, if marginal oil fields are developed, a large amount of energy is used, for example, for setting up the oil delivery system and / or for the drilling process. However, if it turns out that the marginal oil reservoir does not provide enough recoverable oil, the energy used is wasted, because the complete oil delivery system must be transported to the new oil reservoir and the energy-intensive drilling process must be carried out again. This leads in particular to increased wear on the drilling equipment, because it now has to be used for additional drilling operations in an unplanned manner. Therefore, in this case, for example, the drill bit must be replaced earlier or needs to undergo resource-intensive maintenance, which in turn leads to an inefficient use of resources in the development of marginal oil reservoirs.
[0006] In order to avoid undesirable wear and tear of drilling equipment and ensure safe operation of oil delivery systems (sometimes referred to as facilities or oil delivery facilities), it is desirable to avoid setting up oil delivery systems at marginal reservoirs with only a small amount of recoverable oil. However, in order to determine whether a marginal reservoir provides a sufficient amount of recoverable oil, petroleum engineers currently rely mainly on their experience, because it is impossible to determine the exact amount of recoverable oil in a marginal reservoir through a reasonable amount of work or with the aid of reasonable measurements. Considering the potential risks of developing marginal reservoirs, it is not enough to decide on development without data-driven evaluation / verification and based solely on educated guesses, because there is a risk of the negative effects discussed above (unsafe operation of an oil delivery system that has moved a lot, unsafe operation due to material compression, and waste of resources for exchange, repair of equipment and / or energy consumption).
[0007] In this context, the object of the present invention is to provide a method for developing efficient resource use of an oil reservoir, which method reduces the risk of inefficient resource use, for example in terms of energy consumption and material wear, which could lead to unsafe operation of an oil transportation system installed at the oil reservoir. Summary of the invention
[0008] The above mentioned problem is at least partly solved by a computer implemented method according to claim 1 , a computer program according to claim 27 , a non-transitory computer readable medium according to claim 28 and a data processing apparatus according to claims 29 and 30 .
[0009] The first embodiment of the present invention is a computer-implemented method for optimizing the use of resources for developing oil reservoirs, the method comprising: receiving one or more input parameters for each of a plurality of oil reservoirs, wherein each of the one or more input parameters indicates a physical property of the corresponding oil reservoir; generating a reservoir profile for each of the plurality of oil reservoirs based on the corresponding one or more input parameters; wherein the reservoir profile comprises at least one probability profile; based on the generated reservoir profiles, sorting the plurality of oil reservoirs according to a sorting scheme comprising one or more sorting parameters; and generating a probability distribution of the amount of existing oil reserves of the reservoir for evaluating the correctness of the sorting for each of the plurality of oil reservoirs.
[0010] Providing this method can allow for efficient selection of reservoirs to be developed. The generated reservoir profile can form a solid basis for decision making based on a ranking scheme that prioritizes different aspects of the reservoir (e.g., drilling complexity, the amount of recoverable oil reserves, etc.). Generating additional probability distributions can allow for further evaluation of the ranking. Therefore, a robust method is disclosed that reduces the risk of inefficient resource use due to the selection of inappropriate reservoirs that only provide insufficient amounts of recoverable oil. In other words, the method can lead to the appropriate selection of marginal reservoirs, thereby preventing waste of resources caused by unnecessary assembly and disassembly of the oil transportation system. In addition, since the appropriate selection of marginal reservoirs can reduce the number of drilling processes as described above, energy waste can be prevented. In addition, since the reduction in the number of assembly and disassembly processes reduces the possibility of incorrect installation of the oil transportation system, safety can be improved.
[0011] According to a second embodiment, the one or more input parameters include at least one of: one or more measured parameters of the reservoir; and / or one or more pressure-volume-temperature (PVT) parameters of the reservoir.
[0012] Providing additional information about the reservoir allows for more accurate reservoir profiles and probability distributions to be generated. Thus, the risk of selecting an inappropriate reservoir for development is further reduced, leading to an overall optimization of resource usage for reservoir development.
[0013] According to a third embodiment, the one or more measured parameters include at least one of: dissolved gas-oil ratio (GOR); oil specific gravity; gas specific gravity; water salinity; molar percentage of hydrogen sulfide (H2S); molar percentage of carbon dioxide (CO2); and / or molar percentage of nitrogen (N2).
[0014] According to a fourth embodiment, the one or more PVT parameters include at least one of: temperature within the reservoir; bubble point pressure; pressure within the reservoir; GOR; oil formation volume fraction (FVF); and / or gas FVF.
[0015] Further specifying the measurement parameters and / or PVT parameters allows even more accurate generation of reservoir profiles and probability distributions. Thus, the risk of selecting an inappropriate reservoir for development is further reduced. Thus, the overall resource usage of reservoir development is improved.
[0016] According to a fifth embodiment, wherein the probability profile comprises: a Storage Tank Oil In Place (STOIIP) estimate of the amount of existing oil reserves in the reservoir; and a confidence level of the STOIIP estimate. The STOIIP may also be referred to as OIIP or OIP.
[0017] Associating the STOIIP estimate of the existing oil reserves in a reservoir with the confidence level of the estimate allows for better interpretability of the estimate. Thus, both the ranking and the evaluation of the reservoirs are improved, since the profile now also covers the uncertainty factor of the estimate. If the estimated reserves of a reservoir with a very high confidence interval already allow for efficient resource use, the reservoir can be developed with minimal risk of wasting resources.
[0018] According to a sixth embodiment, the confidence level includes: a P10 confidence level of the STOIIP estimate of the amount of existing oil reserves in the oil reservoir; a P50 confidence level of the STOIIP estimate of the amount of existing oil reserves in the oil reservoir; and / or a P90 confidence level of the STOIIP estimate of the amount of existing oil reserves in the oil reservoir.
[0019] Providing the opportunity to define confidence levels for estimates allows for a flexible and customizable granularity of estimates. This allows for an optimized risk assessment and therefore results in a higher probability of selecting the best reservoir for development.
[0020] According to a seventh embodiment, a reservoir profile is generated based on a material balance approach.
[0021] The use of material balance methods to generate reservoir profiles increases the accuracy of profile generation. The method is able to process given input parameters describing the physical properties of the reservoir to derive accurate characteristics of the reservoir (e.g., reservoir profile). The use of these accurate reservoir profiles reduces the risk of selecting inappropriate reservoirs for development and instead selects the best reservoirs that allow for efficient resource use.
[0022] According to an eighth embodiment, each reservoir profile also includes reservoir specifications, wherein the reservoir specifications include at least one of: reservoir type; reserves in place, the ratio between the amount of existing oil reserves in the reservoir estimated by STOIIP with the lowest confidence level and the amount of existing oil reserves in the reservoir estimated by STOIIP with the highest confidence level; recovery factor range; area of the reservoir; STOIIP density as the ratio between the amount of existing oil reserves in the reservoir estimated by STOIIP and the area of the reservoir; H2S concentration; and / or expected recovery factor.
[0023] The representativeness of the reservoir properties is improved by specifying one or more reservoir profiles in a given parameter. The better the reservoir profile describes the actual reservoir, the more meaningful further analysis (e.g., ranking the reservoirs based on the corresponding profile) is. This results in an overall increase in resource utilization in reservoir development.
[0024] According to a ninth embodiment, each reservoir profile further includes a reservoir requirement, wherein the reservoir requirement includes information regarding at least one of: aggregated likelihood; average permeability; viscosity; stimulation requirement; and / or production opportunity for each well.
[0025] Providing additional information about reservoir requirements in the reservoir profile allows for a better assessment of the reservoir and therefore leads to an overall increase in resource use. For example, if advanced stimulation techniques are required to extract oil from a reservoir, the complexity of the infrastructure required to extract the oil increases. Incorporating such information into the decision-making process allows for a reduction in the risk of inefficient resource use, as these additional efforts have been accounted for in the process.
[0026] According to a tenth embodiment, the sequencing scheme is selectable from a plurality of sequencing schemes, the plurality of sequencing schemes comprising: an oil production workload scheme; a net present value (NPV) scheme; and / or an accelerated reserve scheme.
[0027] Selectable / customizable ranking schemes allow for a better evaluation of reservoirs with given criteria. For example, depending on the available resources for oil extraction (e.g., specific production stimulation requirements or the availability of certain hardware such as drill bits, etc.), reservoirs can be ranked based on production workload. If a complex reservoir cannot currently be developed due to the unavailability of the necessary drill bits, the reservoirs can be ranked according to an oil production workload scheme. The reservoir with the lowest production workload can then be selected based on the reservoir characteristics described by the reservoir profile. Thus, it can be avoided that during the development of a reservoir it is realized that the development cannot be continued due to the lack of the necessary hardware, equipment and / or tools. This would lead to a very inefficient use of resources, which can be avoided by using the present invention.
[0028] According to an eleventh implementation, each of the one or more sorting parameters of a sorting scheme in the plurality of sorting schemes is assigned an adjustable weight.
[0029] The selection ranking scheme adjusts the weights associated with the corresponding ranking parameters. Thus, an accurate and flexible ranking mechanism is provided, allowing for improved ranking of reservoirs. This allows for the selection of the best reservoirs for development under given circumstances. In this way, the resources of the developed reservoirs can be used efficiently.
[0030] According to a twelfth embodiment, the one or more ranking parameters include at least one of: the amount of expected reserves of the reservoir; the uncertainty density of the reservoir; the STOIIP density of the reservoir; the surface complexity of the reservoir; the drilling complexity of the reservoir; the H2S content of the reservoir; the maturity of the reservoir; the data availability of the reservoir; and / or the economic rating of the reservoir.
[0031] According to a thirteenth embodiment, the economic rating is based on at least one of the NPV of the oil of the reservoir and / or the unit technical cost of the reservoir.
[0032] Considering one or more ranking parameters enables a more accurate ranking of reservoirs. Thus, reservoirs that allow for rational resource utilization can be identified.
[0033] According to a fourteenth embodiment, data availability for a reservoir indicates a rate of acquired data per square kilometer and is based on at least one of: one or more penetration well logs; one or more penetration well tests; multiple dedicated wells; multiple dedicated evaluations; and / or the area of the reservoir.
[0034] Data availability represents a factor in assessing the significance of a reservoir. If a reservoir is associated with high data availability, then the corresponding reservoir profile is more likely to be meaningful. However, if a reservoir is associated with low data availability, then the reservoir profile is more likely to not describe the true properties of the reservoir. Therefore, reservoirs with high data availability will be preferred in the corresponding ranking scheme, because high levels of uncertainty involve the risk of inefficient resource usage.
[0035] According to a fifteenth embodiment, the surface complexity of the reservoir is based on at least one of: facility requirements; fluid compatibility; well acceptance; hitch requirements; and / or health safety environment (HSE) considerations.
[0036] The surface complexity of a reservoir describes the amount of resources and effort expended to develop the reservoir. Reservoirs beneath highly rugged surfaces may have a high surface complexity. Building production infrastructure on such surfaces may be complex, given the high safety requirements that the facilities must meet (e.g., HSE considerations). Taking this factor into account improves the accuracy of reservoir sequencing and, therefore, improves the efficiency of resource use.
[0037] According to a sixteenth embodiment, the drilling complexity is based on at least one of: a subsurface obstruction; an identified drilling complexity; a tight formation in the reservoir; and / or a necessity for advanced stimulation techniques.
[0038] Drilling complexity is another factor that describes the amount of resources and effort spent to develop a reservoir. Due to high drilling complexity, a reservoir surrounded by solid rock may require the use of a special drill bit for solid rock. In contrast, a reservoir associated with low drilling complexity can be developed using a normal drill bit. However, if information about the complexity is not available / included in the development decision, and someone makes the mistake of using a normal drill bit on a solid rock formation, this can result in severe damage to the drill bit. Therefore, considering this factor in the ranking allows for optimization of resource usage.
[0039] According to a seventeenth embodiment, a value of a ranking parameter of the one or more ranking parameters is determined based at least on one or more input parameters and / or a reservoir profile.
[0040] Determining the value of one or more ranking parameters individually for each reservoir improves the accuracy of the ranking and, therefore, improves the efficiency of resource use.
[0041] According to an eighteenth embodiment, generating a probability distribution of the amount of oil reserves of the reservoir includes: determining a simulation configuration; and based on the simulation configuration, generating a probability distribution of STOIIP estimates of the amount of oil reserves of the reservoir using Monte Carlo simulation.
[0042] Monte Carlo simulation is used to generate a probability distribution based on a given simulation configuration resulting in a STOIIP estimate of the amount of oil reserves that is independent of the reservoir profile. Thus, an additional step of assessing the correctness of the ranking is provided. This increases the overall accuracy of the method in identifying suitable reservoirs for development under improved resource use.
[0043] According to the nineteenth embodiment, determining the simulation configuration includes: selecting a STOIIP estimation formula from one or more estimation formulas; and selecting a sample distribution for at least one sensitivity of the selected estimation formula, preferably one of a uniform distribution, a triangular distribution, a normal distribution or a lognormal distribution.
[0044] The estimation formula and sample distribution are selected for their corresponding sensitivity (i.e., the variable of the formula) to further minimize the uncertainty risk of the estimation. By sampling n values for each sensitivity based on the selected sample distribution, the risk of statistical outliers that will have a negative impact on the estimation result is reduced. The amount n of sampling iterations can be predefined or configurable. Therefore, the overall accuracy of the simulation is increased, which leads to improved evaluation of the ranking.
[0045] According to a twentieth embodiment, the STOIIP estimation formula includes at least one or more of the following sensitivities: the gross rock volume (GRV) of the reservoir; the net-to-gross ratio (NVG) of the reservoir; the area of the reservoir; the net thickness of the reservoir; the porosity of the reservoir; the water saturation of the reservoir; and / or the initial oil formation volume coefficient.
[0046] Specifying the sensitivity of the estimator allows covering all necessary factors that potentially affect the simulation. Therefore, covering these factors with the corresponding sample distributions further minimizes the uncertainty risk of the simulation.
[0047] According to a twenty-first embodiment, one or more sensitivity values are determined based at least on one or more input parameters and / or a reservoir profile.
[0048] Determining the value of one or more sensitivities based on the input parameters and / or reservoir profile individually allows for generating samples from the selected distribution at more accurate starting values (i.e., seeds) that take into account a given property of the reservoir. Thus, the overall simulation results (i.e., the amount of estimated oil reserves) are improved.
[0049] According to the twenty-second embodiment, a first of the one or more estimation equations is specified as:
[0050]
[0051] According to the twenty-third embodiment, the second formula is specified as:
[0052]
[0053] The selection of the corresponding estimator may be based on the sensitivity that is intended to be validated using sampling. If GRV is the main interest for the validation, then the first equation may be appropriate. If the area and thickness of the reservoir are of main interest, then the second equation may be appropriate. In cases where multiple sensitivities are intended to be validated, several estimators may also be combined.
[0054] According to a twenty-fourth embodiment, evaluating the correctness of the ranking includes verifying the correctness of at least one probability profile for each reservoir of the plurality of reservoirs.
[0055] Validating the probability profile based on the probability distribution allows determining whether the probability profile is reasonable. Thus, if the probability profile of a reservoir profile does not appear reasonable (e.g., the estimator of the probability profile is not covered by the probability distribution), the probability profile can be excluded from the ranking. Thus, the robustness of the ranking can be improved, resulting in an increase in the overall efficiency of resource use for reservoir development.
[0056] According to the twenty-fifth embodiment, the verification includes: for each probability profile, extracting a STOIIP estimate of the amount of existing oil reserves in the reservoir from a probability distribution having the same confidence level as the corresponding probability profile; calculating the difference between the amount from the probability distribution and the amount from the probability profile; and if the difference is less than a predefined threshold, determining the correctness of the probability profile.
[0057] Validation of the probability profile can be achieved by extracting an estimator from a probability distribution with the same confidence level and determining the difference between the two estimators. If the difference is less than a predefined threshold (e.g., a predefined percentage of the estimator), the probability profile can be considered realistic. If not, the profile can be excluded from the ranking.
[0058] According to a twenty-sixth embodiment, the method further comprises: selecting a reservoir for development based on the ranking or the evaluated ranking.
[0059] The selection of reservoirs for development based on the ranking or based on an assessed ranking constitutes an optional step of the disclosed method.Thanks to the preceding method steps, reservoirs can be selected that enable a rational resource use.
[0060] The twenty-seventh embodiment is a computer program including instructions which, when executed by a computer, cause the computer to perform the method according to any one of the first to twenty-sixth embodiments.
[0061] The twenty-eighth embodiment is a non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a computer, causes the computer to perform the method according to any one of the first to twenty-sixth embodiments.
[0062] The twenty-ninth embodiment is a data processing device, which includes a device configured to execute the method according to any one of the first to twenty-sixth embodiments.
[0063] The device may be a common personal computer, a laptop or a known processing system comprising one or more processors and a memory.
[0064] The thirtieth embodiment is a data processing device, which includes: a memory; and one or more processors operably coupled to the memory, the memory and the one or more processors being configured to execute the method according to any one of the first to twenty-sixth embodiments. BRIEF DESCRIPTION OF THE DRAWINGS
[0065] Various embodiments of the present invention are described in more detail below with reference to the drawings, but the present invention is not limited to the embodiments of these drawings.
[0066] Figure 1 An overview of an oil field comprising a plurality of oil reservoirs according to an embodiment of the invention is shown.
[0067] Figure 2 A method for optimizing the use of resources for developing an oil reservoir according to an embodiment of the present invention is shown.
[0068] Figure 3a to Figure 3c The general structure of a reservoir profile according to an embodiment of the present invention is shown.
[0069] Figure 4a to Figure 4b A probability distribution of the amount of existing oil reserves according to an embodiment of the present invention is shown.
[0070] Figure 5 A graphical user interface (GUI) of a computer program according to an embodiment of the present disclosure is shown.
[0071] Throughout the drawings and description, the same reference numerals refer to the same elements. In the drawings, the reference numerals are shown by way of example, rather than limiting the embodiments of the drawings to include only the reference numerals shown. DETAILED DESCRIPTION
[0072] Hereinafter, exemplary embodiments of the present invention are described in more detail.
[0073] Figure 1 An overview of an exemplary oil field 100 is depicted. The oil field in this example includes three reservoirs 110a, 110b, and 110c. Each of these reservoirs 110a to 110c may include a certain amount of oil reserves. For example, reservoir 110a may include a large amount of oil reserves, making it reasonable to install three oil wells. Reservoir 110b may contain a smaller amount of oil reserves, which makes it unreasonable to install more than two oil wells. Reservoir 110c may not be developed yet. Determining whether reservoir 110c can be efficiently developed in terms of resource use can be achieved by applying the methods under the present disclosure.
[0074] Figure 2 A flow chart of a method 200 for optimizing development of resources of reservoirs 110a - 110c is depicted, in accordance with an embodiment of the present invention.
[0075] In step 210 of method 200, one or more input parameters are received for each reservoir 110a to 110c of a plurality of reservoirs 110a to 110c, wherein each of the one or more input parameters indicates a physical property of the corresponding reservoir. The one or more input parameters may include at least one of (one or more) measured parameters and / or (one or more) PVT parameters of the reservoirs 110a to 110c. Table 1 provides an overview of possible measured parameters and PVT parameters for the reservoirs 110a to 110c. It should be understood that these are merely exemplary parameters.
[0076] Table 1
[0077] Measurement parameters PVT parameters Solution gas-oil ratio Temperature in the reservoir Oil specific gravity Bubble point pressure Gas specific gravity Pressure in the reservoir Water salinity Gas-Oil Ratio Mole percentage of hydrogen sulfide Oil formation volume factor Mole percent of carbon dioxide Gas formation volume factor Mole percent nitrogen
[0078] In step 220, a reservoir profile 300 is generated for each of the plurality of reservoirs 110a to 110c based on the corresponding one or more input parameters, wherein the reservoir profile 300 includes at least one probability profile 340. The probability profile 340 may include a STOIIP estimate 342 of the amount of existing oil reserves in the reservoirs 110a to 110c and a confidence level 342 of the STOIIP estimate. These confidence levels 342 may include a P10 confidence level, a P50 confidence level, and / or a P90 confidence level. Thus, in this example, three probability profiles 340 may be generated, wherein a first probability profile 340 includes a first STOIIP estimate of the amount of oil reserves in the reservoirs 110a to 110c and a first confidence level 342, i.e., a P10 (i.e., 10% probability) confidence level of the corresponding STOIIP estimate. In this case, the confidence level P10 indicates the possibility that the corresponding estimated oil volume can actually be obtained from the corresponding reservoirs 110a to 110c. In the case of P10, the estimated oil volume can be obtained in 10% of the cases. The P50 (also called Pmean) confidence level indicates the value that can be obtained in 50% of the cases, and P90 indicates the value that can be obtained in 90% of the cases.
[0079] A material balance method may be used to generate the reservoir profile 300. The corresponding material balance method may be, for example, a material balance method such as provided by MBAL PETEX software. Each reservoir profile 300 may include a reservoir specification 360, which includes at least one of the following: a reservoir type 362 (e.g., acid or non-acid); in-place reserves 364; an area 370 of the reservoir 110a to 110c (e.g., in km2 measured); a ratio 366 between the amount of existing oil reserves in the reservoirs 110a to 110c estimated by the STOIIP with the lowest confidence level (e.g., P10) 342 and the amount of existing oil reserves in the reservoirs 110a to 110c estimated by the STOIIP with the highest confidence level (e.g., P90) 342; a STOIIP density 372 which is a ratio between the amount of existing oil reserves in the reservoirs 110a to 110c estimated by the STOIIP and the area 370 of the reservoirs 110a to 110c; a hydrogen sulfide concentration 374 and / or an expected recovery factor 376. The reservoir profile 300 may also include a reservoir requirement 380, which includes information about at least one of: an aggregated likelihood 382 (e.g., yes / no, or a probability that it is possible or not possible); a production opportunity 390 for each well (i.e., how much oil each well can extract from the reservoirs 110a to 110c); a viscosity 386 (e.g., the viscosity of the oil in the reservoirs 110a to 110c); an increased production requirement 388 for extracting oil from the reservoirs 110a to 110c (e.g., acid fracturing, hydraulic fracturing, etc.) and / or an average permeability 384. Each reservoir profile 300 may also include an asset selection 320, which includes information for identifying the corresponding reservoir 110a to 110c (e.g., an operating company (OpCo) 322, an asset 324, a field name 326 of the field 100, a reservoir name 328 of the reservoirs 110a to 110c, and / or a unique identifier 330 of the reservoirs 110a to 110c, which may be generated by concatenating the field name 326 and the reservoir name 328).
[0080] In step 230, based on the generated reservoir profile 300, multiple reservoirs 110a to 110c are sorted according to a sorting scheme, and the sorting scheme includes one or more sorting parameters. The sorting scheme can be selectable from multiple sorting schemes. Multiple sorting schemes can include oil production workload schemes, net present value (NPV) schemes and / or accelerated reserve schemes. When the oil production workload scheme is selected, multiple reservoirs 110a to 110c are sorted according to their estimated workload for extracting oil. For example, if it is easier to extract oil from reservoir 110a than from reservoir 110b, and it is easier to extract oil from reservoir 110b than from reservoir 110c, then reservoir 110a will be sorted as the first, reservoir 110b is sorted as the second, and reservoir 110c is sorted as the third. When the NPV scheme is selected, reservoirs 110a to 110c are sorted according to the expected net income of the estimated extractable oil volume from reservoirs 110a to 110c. When the accelerated reserve scenario is selected, the reservoirs 110a-110c are ranked according to the likelihood and / or amount of accelerated reserve of the reservoirs 110a-110c.Additional scenarios may be customizable and may be added to the plurality of ranking scenarios prior to or during execution of the method.
[0081] The one or more ranking parameters may include at least one of: the amount of expected reserves of the reservoirs 110a to 110c; the uncertainty density of the reservoirs 110a to 110c; the STOIIP density of the reservoirs 110a to 110c; the surface complexity of the reservoirs 110a to 110c; the drilling complexity of the reservoirs 110a to 110c; the H2S content of the reservoirs 110a to 110c; the maturity of the reservoirs 110a to 110c; the data availability of the reservoirs 110a to 110c and / or the economic rating of the reservoirs 110a to 110c. The economic rating may be based on at least one of the NPV of the reservoirs 110a to 110c and / or the unit technical cost of the reservoirs 110a to 110c. The data availability may indicate a rate of acquiring data per square kilometer and be based on at least one or more penetration well logs, one or more penetration well tests, a number of dedicated wells, a number of dedicated evaluations and / or the area of the reservoirs 110a to 110c. Data availability can be calculated using the following formula:
[0082]
[0083] The surface complexity of the reservoirs 110a to 110c can be based on at least one of: facility requirements (e.g., wells, pipelines, tanks, injection facilities, and control units), fluid compatibility, well reception, hit-and-go requirements, and / or health safety environment (HSE) considerations. One or more of these factors can be combined to generate an overall surface complexity rating. The surface complexity rating can be a rating between 1 (complex surface) and 5 (low complexity). Drilling complexity can be based on at least one of: underground obstructions; identified drilling complexity; tight formations in the reservoirs 110a to 110c and / or the necessity of advanced stimulation techniques. One or more of these factors can be combined to generate an overall drilling complexity rating. The drilling complexity rating can be a rating between 1 (high complexity) and 5 (low complexity). Determining the value of one of these ranking parameters can be based on one or more input parameters, the reservoir profile 300, and / or additional inputs.
[0084] In step 240, for each of the plurality of reservoirs 110a to 110c, a probability distribution 400a to 400b of the amount of existing oil reserves of the reservoirs 110a to 110c is generated for evaluating the correctness of the ranking. Generating the probability distribution 400a to 400b of the amount of oil reserves of the reservoirs 110a to 110c may include: determining a simulation configuration; and generating the probability distribution 400a to 400b of the STOIIP estimate of the amount of oil reserves of the reservoirs 110a to 110c using Monte Carlo simulation based on the simulation configuration. Determining the simulation configuration may include: selecting a STOIIP estimator from one or more estimators; and selecting a sample distribution (e.g., a uniform distribution, a triangular distribution, a normal distribution, a lognormal distribution, or any other distribution) for at least one sensitivity of the selected estimator.
[0085] The STOIIP estimation formula may include at least one or more of the following: the total rock volume of the reservoirs 110a to 110c; the net-to-gross ratio of the reservoirs 110a to 110c; the area of the reservoirs 110a to 110c; the (net) thickness of the reservoirs 110a to 110c; the porosity of the reservoirs 110a to 110c; the water saturation and / or the initial oil formation volume factor of the reservoirs 110a to 110c. The value of one or more sensitivities may be based on at least one of the one or more input parameters and / or the reservoir profile 300. For example, according to the reservoir profile 300 110a to 110c, the area value of the reservoir may be 10km 2 However, this value may be incorrect, for example due to measurement inaccuracies or other errors affecting the result. To generate a statistical equilibrium value for the reservoir area, the selected sampling distribution may be used to generate, for example, 1000 sampling values. According to the value of the profile reservoir, in this example 10 km 2, which can be used as a seed for sampling. These 1000 sampled values can then be used to generate 1000 STOIIP estimates, thereby generating probability distributions 400a to 400b of STOIIP estimates. In this way, the risk of incorrect or outliers can be avoided. This risk can be further reduced by performing this step on more than one of the sensitivities of the estimate. The first estimate can be specified as
[0086] Formula I:
[0087]
[0088] The second estimator can be specified as
[0089] Formula II:
[0090]
[0091] Therefore, in the case of formula 1, the sensitivity GRV, porosity, water saturation and initial oil formation volume coefficient can be sampled according to the above procedure. In the case of formula 2, the sensitivity area, thickness, water saturation and initial oil formation volume coefficient can be sampled according to the above procedure.
[0092] Evaluating the correctness of the ranking may include verifying the correctness of at least one probability profile 340 for each reservoir 110a to 110c. The verification may include extracting, for each probability profile 340, a STOIIP estimate 410, 420, 430 of the amount of existing oil reserves of the reservoirs 110a to 110c from a probability distribution having the same confidence level as the corresponding probability profile 340. The difference between the amount 410, 420, 430 from the probability distribution 400a to 400b and the amount from the probability profile 340 may be calculated. If the difference is less than a predefined threshold, the correctness of the probability profile 340 may be determined. Based on the (evaluated) ranking, the reservoirs 110a to 110c may be selected for development.
[0093] It should be noted that although shown in detail this is only an exemplary sequence of method steps. It should be understood that the order of the method steps can be changed, and some method steps can even be performed in parallel. In addition, it should be understood that the general principles underlying this method can also be applied to optimize the use of resources for developing other types of reservoirs (e.g., gas reservoirs). Therefore, the present invention also covers these other types of reservoirs.
[0094] Figure 3a to Figure 3c 2 shows a general structure of a reservoir profile 300 according to an embodiment of the present invention. The reservoir profile 300 may be generated according to the method 200. Figure 3aThe illustrated reservoir profile 300 may include / contain asset selections 320, probability profiles 340, reservoir specifications 360, and reservoir requirements 380. The asset selections 320 may include information for identifying the corresponding reservoirs 110a to 110c. The information may include an operating company (OpCo) 322, an asset 324, a field name 326 of the field 100, a reservoir name 328 of the reservoirs 110a to 110c, and / or a unique identifier 330 of the reservoirs 110a to 110c, which may be generated by concatenating the field name 326 and the reservoir name 328. The (at least one) probability profile may include a STOIIP estimate of the amount of oil reserves in the reservoirs 110a to 110c and a confidence level 342 of the (corresponding) STOIIP estimate.
[0095] like Figure 3b The reservoir specifications 360 shown may include reservoir type 362, reserves in place (e.g., this may refer to the amount of oil that has been extracted from reservoirs 110a to 110c), a ratio 366 between the amount of existing oil reserves in reservoirs 110a to 110c with the STOIIP estimate having the lowest confidence level 342 and the amount of reserves in reservoirs 110a to 110c with the STOIIP estimate having the highest confidence level 342 (e.g., P10 / P90), a recovery factor range 368, an area 370 of reservoirs 110a to 110c, a STOIIP density as a ratio between the amount of existing oil reserves in reservoirs 110a to 110c with the STOIIP estimate 342 and the area 370 of reservoirs 110a to 110c, an H2S concentration 374, and / or an expected recovery factor 376.
[0096] like Figure 3c The reservoir requirements 380 shown may include aggregated likelihood 382 , average permeability 384 , viscosity 386 , stimulation requirements 388 , and / or production opportunities 390 for each well.
[0097] Figure 4a to Figure 4b Probability distributions 400a to 400b of the amount of existing oil reserves according to an embodiment of the present invention are shown. The probability distributions 400a to 400b may be generated in step 240 of method 200. Thus, the distributions 400a to 400b shown are generated using Monte Carlo simulation based on a determined simulation configuration including a corresponding STOIIP estimation formula (e.g., Formula I or II) including one or more sensitivity and sample distributions. Figure 4a to Figure 4bDepicted are exemplary distributions 400a to 400b generated using approximately 5000 samples. Each sample represents an estimated STOIIP. At the bottom scale, the estimated STOIIP range is shown as from 0.00STOIIP to 75.76STOIIP. The left scale depicts the number of samples for which the corresponding STOIIP amount has been estimated. For example, approximately 210 samples out of a total of 5000 samples have an estimated STOIIP amount of 5.85. The right scale depicts the cumulative distribution. For example, approximately 90% of the 5000 samples estimate the STOIIP to be equal to or less than 46.63 (430). Approximately 50% of the 5000 samples estimate the STOIIP to be equal to or less than 23.32 (420). Finally, approximately 10% of the 5000 samples estimate the STOIIP to be equal to or less than 5.85 (410).
[0098] According to the method 200, such distributions 400a to 400b may be used to assess the correctness of the ranking. This may be done by verifying the correctness of at least one probability profile 340 for each reservoir 110a to 110c of the plurality of reservoirs 110a to 110c. The verification may include: for each probability profile 340, extracting STOIIP estimates 410, 420, 430 of the amount of existing oil reserves of the reservoirs 110a to 110c from the probability distribution 300 having the same confidence level 342 as the corresponding probability profile 340; calculating the difference between these amounts; and determining the correctness of the probability profile 340 if the difference is less than a predefined threshold. In the following, an example is shown that details these steps of the method 200.
[0099] For example, assume that the reservoir profile 300 includes a first probability profile 340. The first probability profile 340 includes a STOIIP estimate of 50 associated with a 10% confidence level 342. For example, according to the first probability profile 340, (at least) 50 STOIIPs can be extracted in 10% of the cases. In order to verify this probability profile 340, a STOIIP estimate 430 is extracted from a generated probability distribution 400b having the same confidence level (10%) as the probability profile 340. In this example, the extracted STOIIP estimate 430 will be a STOIIP of 46.63. This is because in 90% of the cases, the estimated STOIIP is less than or equal to 46.64. Therefore, in 100%-90%=10% of the cases, the STOIIP is more than 46.63. After extraction, the difference between the two quantities is calculated. In this example, the difference can be calculated by diff=|50-46.63|=3.37 or diff=|46.63-50|=3.37. In order to determine the correctness of the first probability profile 340, the difference is then compared with a predefined threshold. The threshold can be an absolute value (e.g., 10STOIIP) or a relative value (e.g., 10% of the estimated value of the probability distribution 400a to 400b or the probability profile 340, 342). In this example, it is assumed that the threshold is defined as 10% of the value of the probability distribution 400b, so the threshold=0.1×46.63=4.663. Therefore, in this example, the method 200 will determine that the first probability profile 340 is correct because 3.37<4.663. It can be concluded that the rating of the corresponding reservoirs 110a to 110c is correct, which minimizes the risk of inefficient resource use when developing the reservoirs 110a to 110c.
[0100] Figure 5A graphical user interface (GUI) 500 of a computer program according to an embodiment of the present disclosure is shown. The GUI 500 allows a user to select an oil field 100 and corresponding reservoirs 110a to 110c of the oil field 100 via an asset selection 510 option. In addition, the user can obtain information about the location of the oil field 100 via a GUI element oil field location 505. Via GUI elements data update route 515 and model launch route 520, the user can cause the computer program under the GUI 500 to perform a method 200 according to an embodiment of the present disclosure. The user can select a scenario (i.e., probability profile 340) of the selected reservoir 110a to 110c, such as a P50 scenario, via the asset selection 510 option. Depending on the selected scenario, the GUI elements oil production rate 525, injection rate / water cut 530, ARMS (e.g., advanced reservoir modeling and simulation) 535, economics 540, producer / injector counts 545, 550, and corresponding probability distributions 555 with additional summary statistics 575 are updated. The user is also presented with a reservoir ranking 560, such as that resulting from the ranking step of method 200. Also displayed is a currently selected ranking scheme 565 and corresponding weights for ranking parameters 570 (e.g., amount of expected reserves for the selected case, uncertainty density, STOIIP density for the selected case, surface complexity, drilling plugging / complexity, H2S content, reservoir maturity, data availability, and / or economic rating).
[0101] The user is given the option to change the ranking scheme 565 or adjust the weights of one / some / all ranking parameters 570, which results in re-execution of some or all method steps of the method 200 and ultimately results in a new ranking 560 of the selected reservoirs 110a to 110c. The asset selection GUI element 510 may indicate information of the asset selection 320 of the generated reservoir profile 300 (e.g., OpCo 322, Asset 324, Field Name 326, Reservoir Name 328, and / or Field Name + Reservoir Name 330). The selected case of the asset selection GUI element 510 may refer to the probability profile 340 of the generated reservoir profile 300 (i.e., if P10, P50, and P90 probability profiles are generated, the user has the option to choose between these 3 cases). The oil production rate GUI element 525 may indicate the STOIIP estimate with the corresponding confidence level 342 of the probability profile 340 according to the selected case. The STOIIP estimate may indicate the cumulative amount of oil extracted for many years. The GUI element injection rate / water cut 530 may indicate the corresponding amount of water injected into the reservoir 110a to 110c required to extract the oil. The GUI element ARMS 535 may indicate the values of the in-place reserves 364 and the recovery range 368 of the reservoir specification 360 of the corresponding reservoir profile 300. The economics GUI element 540 may indicate values associated with the economics ranking ranking parameters, such as the net present value and / or unit technical cost of the reservoir 110a to 110c. The producer / injector count GUI elements 545, 550 may indicate the number of producers and injectors (e.g., 3) and the corresponding expected recovery factor 376 of the reservoir specification 360 of the corresponding reservoir profile 300. The summary statistics GUI element 575 may indicate values associated with the generated probability distribution 400a to 400b indicated by the GUI element 555. The statistics may include the extracted STOIIP quantities 410, 420, 430 for the confidence level 342 of the generated probability profile 340 for the reservoir profile 300. The statistics may also include values such as standard deviation, mean deviation, variance, and skewness of the indicated probability distributions 400a-400b.
[0102] The computer program may be executed on any suitable data processing apparatus including correspondingly configured devices (e.g., memory, and one or more processors operatively coupled to the memory). The computer program may be stored as computer executable instructions on a non-transitory computer readable medium.
[0103] The embodiments of the present disclosure may be implemented in any of a variety of forms. For example, in some embodiments, the present invention may be implemented as a computer-implemented method, a computer-readable storage medium, or a computer system.
[0104] In some embodiments, a non-transitory computer-readable storage medium may be configured such that it stores program instructions and / or data, wherein the program instructions, if executed by a computer system, cause the computer system to perform a method, for example, any method embodiment described herein, or any combination of the method embodiments described herein, or any subset of any method embodiment described herein, or any combination of such subsets.
[0105] In some embodiments, the computing device may be configured to include a processor (or a group of processors) and a storage medium, wherein the storage medium stores program instructions, wherein the processor is configured to read and execute the program instructions from the storage medium, wherein the program instructions are executable to implement any of the various method embodiments described herein (or any combination of the method embodiments described herein, or any subset of any method embodiments described herein, or any combination of such subsets). The device may be implemented in any of a variety of forms.
[0106] Although specific embodiments have been described above, these embodiments are not intended to limit the scope of the present disclosure, even if only a single embodiment is described for a particular feature. Unless otherwise specified, the examples of features provided in the present disclosure are intended to be illustrative rather than limiting. The above description is intended to cover such alternatives, modifications, and equivalents that will be apparent to those skilled in the art having the benefit of the present disclosure.
[0107] The scope of the present disclosure includes any feature or combination of features disclosed herein (explicitly or implicitly) or any generalization thereof, whether or not it mitigates any or all of the problems addressed herein. In particular, with reference to the appended claims, features from dependent claims may be combined with those of the independent claims, and features from individual independent claims may be combined in any suitable manner, and not merely in the specific combinations listed in the appended claims.
Claims
1. A method for optimizing the development of an oil reservoir (110a; 110b; 110c), the method comprising: For each reservoir (110a; 110b; 110c) of a plurality of reservoirs (110a; 110b; 110c), receiving (210) one or more input parameters, wherein each of the one or more input parameters indicates a physical property of the corresponding reservoir (110a; 110b; 110c); generating (220) a reservoir profile (300) for each reservoir (110a; 110b; 110c) of the plurality of reservoirs (110a; 110b; 110c) based on the corresponding one or more input parameters, wherein the reservoir profile (300) includes at least one probability profile (340); Based on the generated reservoir profile (300), ranking (230) the plurality of reservoirs (110a; 110b; 110c) according to a ranking scheme, the ranking scheme comprising one or more ranking parameters; and For each oil reservoir (110a; 110b; 110c) of the plurality of oil reservoirs (110a; 110b; 110c), a probability distribution (400a; 400b) of the amount of existing oil reserves of the oil reservoir (110a; 110b; 110c) is generated (240) for use in evaluating the correctness of the ranking.
2. The method according to claim 1, wherein: The one or more input parameters include at least one of the following: one or more measured parameters of the reservoir (110a; 110b; 110c); and / or One or more pressure-volume-temperature (PVT) parameters of the reservoir (110a; 110b; 110c).
3. The method according to claim 2, wherein: The one or more measurement parameters include at least one of the following: Solution gas oil ratio GOR; Oil specific gravity; Gas specific gravity; water salinity; Mole percentage of hydrogen sulfide H2S; molar percentage of carbon dioxide CO2; and / or Mole percentage of nitrogen N2.
4. The method according to any one of claims 2 to 3, wherein: The one or more PVT parameters include at least one of the following: the temperature within the oil reservoir (110a; 110b; 110c); Bubble point pressure; the pressure in the oil reservoir (110a; 110b; 110c); GOR; Oil formation volume factor FVF; and / or Gas FVF.
5. The method according to any one of claims 1 to 4, wherein: The probability profile (340) includes: a preliminary STOIIP estimate (342) of the amount of existing oil reserves in the reservoir (110a; 110b; 110c); and The confidence level of the STOIIP estimate (342).
6. The method according to claim 5, wherein: The confidence level (342) includes: a P10 confidence level of the STOIIP estimate of the amount of existing oil reserves of the reservoir (110a; 110b; 110c); a P50 confidence level of the STOIIP estimate of the amount of existing oil reserves of the reservoir (110a; 110b; 110c); and / or The P90 confidence level of the STOIIP estimate of the amount of existing oil reserves of the reservoir (110a; 110b; 110c).
7. The method according to any one of claims 1 to 6, wherein: The reservoir profile (300) is generated based on a material balance method.
8. The method according to any one of claims 5 to 7, wherein: Each reservoir profile (300) also includes a reservoir specification (360), wherein the reservoir specification (360) includes at least one of the following: Reservoir type (362); Reserves in place (364); a ratio (366) between the amount of existing oil reserves in the reservoir estimated by the STOIIP having the lowest confidence level and the amount of existing oil reserves in the reservoir estimated by the STOIIP having the highest confidence level; Recovery factor range (368); The area (370) of the oil reservoir (110a; 110b; 110c); a STOIIP density (372) as a ratio between the amount of existing oil reserves of the reservoir (110a; 110b; 110c) estimated by the STOIIP and the area of the reservoir (110a; 110b; 110c); H2S concentration (374); and / or Expected recovery factor (376).
9. The method according to any one of claims 1 to 8, wherein: Each reservoir profile (300) also includes a reservoir requirement (380), wherein the reservoir requirement (380) includes information regarding at least one of: Possibility of aggregation (382); Average penetration (384); Viscosity (386); Increased production requirements (388); and / or Production opportunities per well (390).
10. The method according to any one of claims 1 to 9, wherein: The sorting scheme is selectable from a plurality of sorting schemes, the plurality of sorting schemes comprising: Oil production workload plan; Net Present Value (NPV) scenario; and / or Accelerate the reserve program.
11. The method according to any one of claims 1 to 10, wherein: Each of the one or more sorting parameters of the sorting scheme of the plurality of sorting schemes is assigned an adjustable weight.
12. The method according to any one of claims 1 to 11, wherein: The one or more sorting parameters include at least one of the following: the amount of expected reserves of the reservoir (110a; 110b; 110c); an uncertain density of the oil reservoir (110a; 110b; 110c); The STOIIP density of the oil reservoir (110a; 110b; 110c); The surface complexity of the reservoir (110a; 110b; 110c); The drilling complexity of the reservoir (110a; 110b; 110c); H2S content of the oil reservoir (110a; 110b; 110c); the maturity of the oil reservoir (110a; 110b; 110c); availability of data on the reservoir (110a; 110b; 110c); and / or An economic rating of the reservoir (110a; 110b; 110c).
13. The method according to claim 12, wherein: The economic rating is based on at least one of an NPV of the reservoir and / or a unit technical cost of the reservoir.
14. The method according to claims 12 to 13, wherein: The data availability of the reservoir (110a; 110b; 110c) indicates a rate of acquired data per square kilometer and is based on at least one of: one or more penetration well logs; one or more penetration well tests; multiple dedicated wells; Multiple dedicated reviews; and / or The area (370) of the oil reservoir (110a; 110b; 110c).
15. The method according to any one of claims 12 to 14, wherein: The surface complexity of the reservoir (110a; 110b; 110c) is based on at least one of the following: Facility requirements; Fluid compatibility; Well receiving; Meet the fixed port requirements; and / or Health, safety and environment (HSE) considerations.
16. The method according to any one of claims 12 to 15, wherein: The drilling complexity is based on at least one of the following: underground blockage; identified drilling complexities; a dense formation in the reservoir (110a; 110b; 110c); and / or The necessity of advanced production enhancement technologies.
17. The method according to any one of claims 1 to 16, wherein: A value of a ranking parameter of the one or more ranking parameters is determined based at least on the one or more input parameters and / or the reservoir profile (300).
18. The method according to any one of claims 1 to 17, wherein: Generating the probability distribution (400a; 400b) of the amount of the oil reserve of the oil reservoir (110a; 110b; 110c) comprises: determine the simulation configuration; and Based on the simulation configuration, the probability distribution (400a; 400b) of the STOIIP estimate of the amount of the oil reserve of the reservoir (110a; 110b; 110c) is generated using Monte Carlo simulation.
19. The method according to claim 18, wherein: Determining the simulation configuration includes: selecting a STOIIP estimator from one or more estimators; and A sample distribution is selected for at least one sensitivity of the selected estimator, preferably one of a uniform distribution, a triangular distribution, a normal distribution or a lognormal distribution.
20. The method according to claim 19, wherein: The STOIIP estimation formula includes at least one or more of the following sensitivities: The total rock volume GRV of the oil reservoir (110a; 110b; 110c); The net-to-gross ratio NVG of the oil reservoir (110a; 110b; 110c); The area (370) of the oil reservoir (110a; 110b; 110c); the net thickness of the oil reservoir (110a; 110b; 110c); the porosity of the oil reservoir (110a; 110b; 110c); The water saturation of the reservoir (110a; 110b; 110c); and / or Initial oil formation volume factor.
21. The method according to claim 20, wherein: Values of the one or more sensitivities are determined based at least on the one or more input parameters and / or the reservoir profile (300).
22. A method according to any one of the preceding claims 19 to 21, wherein: A first of the one or more estimation equations is specified as:
23. A method according to any one of the preceding claims 19 to 22, wherein: The second formula is specified as:
24. The method according to any one of claims 1 to 23, wherein: Assessing the correctness of the ranking includes verifying the correctness of the at least one probability profile (340) for each reservoir (110a; 110b; 110c) of the plurality of reservoirs (110a; 110b; 110c).
25. The method according to claim 24, wherein: Validation includes: for each probability profile (340), extracting a STOIIP estimate (410; 420; 430) of the amount of existing oil reserves of the reservoir (110a; 110b; 110c) from the probability distribution (400a; 400b) having the same confidence level (342) as the corresponding probability profile (340); calculating a difference between a quantity (410; 420; 430) from the probability distribution (400a; 400b) and a quantity (342) from the probability profile (340); and If the difference is less than a predefined threshold, the correctness of the probability profile (340) is determined.
26. The method according to any one of claims 1 to 25, wherein: The method further comprises: Based on the ranking or the assessed ranking, the reservoir (110a; 110b; 110c) is selected for development.
27. A computer program comprising instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 26.
28. A non-transitory computer readable medium storing computer executable instructions which, when executed by a computer, cause the computer to perform the method according to any one of claims 1 to 26.
29. A data processing apparatus comprising means configured to perform the method according to any one of claims 1 to 26.
30. A data processing device, comprising: Memory; as well as One or more processors operably coupled to the memory, the memory and the one or more processors being configured to perform the method according to any one of claims 1 to 26.