Method for quantitatively determining water-drive oilfield development and production allocation yield completion probability

Abstract

The invention relates to the technical field of oil exploration and development, in particular to a method for quantitatively determining the completion probability of water-drive oilfield development and production allocation. The method comprises the steps of selecting a target water-drive oil field and determining a target research area, analyzing uncertainty factors influencing the yield scale of the target water-drive oil field and extracting key indexes, and counting historical development data of batch wells in the current year in the target research area, quantitative characterization is carried out on the key indexes, expected yield values of the target water-driven oil field under different probability conditions are calculated, the risk degree range of the production allocation yield of the target water-driven oil field is obtained, and a risk assessment model of the production allocation yield of the target water-driven oil field is established according to the range; and reasonably predicting the yield of the next target water-driven oil field to complete the task according to risk analysis and evaluation of the model on the production allocation yield of the target water-driven oil field. According to the method, the feasibility target of the target water drive oil field is formulated through scientific guidance, the yield risk is avoided in time, and planning deployment is adjusted in time.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and particularly to a method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation Background Art

[0002] The oilfield development plan is the guiding principle and guideline for the scientific and reasonable development of the oilfield, and plays an important leading guiding role in the oilfield exploitation process. The core of the development plan is to scientifically determine the oilfield production scale

[0003] However, due to the complexity of underground rock, fluid physical properties, and dynamic changes in production status, there are a large number of uncertainties in the oilfield production scale, such as the uncertainty of the underground oil reservoir fluid seepage law change, the uncertainty of the development technology and the effect of oil production enhancement measures, etc. These factors seriously affect the accuracy of the oilfield development plan and production allocation plan. At present, there is no perfect quantitative risk assessment method for the production completion plan. The existing planning plan risk assessment mainly focuses on macroscopic and single subjective qualitative analysis. The existing method mainly uses sensitivity analysis to determine the influence degree of a certain factor change on the production index. However, uncertainty analysis is not equal to risk analysis. Uncertainty is difficult to measure, and sensitivity analysis cannot solve the problem of how likely these factors will change. Therefore, in view of the above deficiencies, a method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation is proposed Summary of the Invention

[0004] (I) Technical Problems to be Solved

[0005] The present invention provides a method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation to overcome the problems in the prior art that due to the inability of sensitivity analysis to solve the changes in influencing factors, it is impossible to scientifically guide the formulation of feasible goals, the production risk is too high, and the planning and deployment adjustment is cumbersome

[0006] (II) Technical Solutions

[0007] To solve the above problems, the present invention provides a method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation, including

[0008] Step S1: Select the target waterflooding oilfield, analyze the uncertain factors affecting the production scale of the target waterflooding oilfield, and extract key indicators according to the obtained uncertain factors

[0009] Step S2: Select the target research area according to the target waterflooding oilfield determined in Step S1, count the historical development data of the current year's batch of wells in the target research area, and quantitatively characterize the key indicators obtained in Step S1

[0010] Step S3: Calculate the expected production values of the target waterflooding oilfield under different probability conditions based on the quantization characterization obtained in Step S2, and obtain the risk degree range of the production allocation of the target waterflooding oilfield;

[0011] Step S4: Establish a risk assessment model for the production allocation of the target waterflooding oilfield based on the risk degree range obtained in Step S3;

[0012] Step S5: Analyze and evaluate the risk of the production allocation of the target waterflooding oilfield according to the risk assessment model in Step S4, and reasonably predict the completion of the production task of the target waterflooding oilfield next time.

[0013] Preferably, in Step S1, by analyzing the production and production allocation parameters of the target waterflooding oilfield, the uncertain factor is the waterflooding production, and the waterflooding production includes the production of old wells without measures, the production of old wells with measures, and the production of new wells.

[0014] Preferably, in Step S1, the key indicators include the decline rate of old wells, the annual oil increment of single-well measures, and the daily oil production of new wells per well.

[0015] Preferably, in Step S2, the regional types of the batch wells in the target study area this year include the basic well pattern, the first-order well pattern, the second-order well pattern, and the third-order well pattern, and the time range of the historical development data is 5-10 years.

[0016] Preferably, in Step S2, the quantization characteristics of the key indicators include the distribution type function, mean value, and variance of the decline rate of old wells; the oil increment effect of fracturing single wells, the oil increment effect of three-replacement single wells, the oil increment effect of perforation-completion single wells, and the oil increment effect of other single wells; the daily oil production of new wells in the old area per well, the daily oil production of peripheral vertical wells per well, and the daily oil production of peripheral horizontal wells per well.

[0017] Preferably, in Step S3, the method for obtaining the risk degree range includes the Monte Carlo random simulation method and the risk variable simulation method. The steps for obtaining the risk degree range are as follows: first, obtain random numbers through the Monte Carlo random simulation method, and then obtain the risk degree range through the risk variable simulation method according to the random number generation method.

[0018] Preferably, the theoretical basis of the Monte Carlo random simulation method is the law of large numbers and the central limit theorem in probability theory; the risk variable simulation method includes the simulation method of risk variables subject to uniform distribution, the simulation method of risk variables subject to normal distribution, and the simulation method of risk variables subject to triangular distribution.

[0019] Preferably, in Step S4, the model calculation formula of the risk assessment model is the production simulation function expression, and its expression is:

[0020]

[0021] Wherein:

[0022] —— represents all measures per year and the increased oil production of new wells;

[0023] —— represents the declining production of old wells per year;

[0024] Q hx (t) —— the aftereffect production of new wells in the t-th year at the beginning of the plan.

[0025] Preferably, in the step S5, the evaluation method of the risk analysis and evaluation includes a risk estimation method, a frequency analysis method, and risk evaluation indicators.

[0026] Preferably, the risk evaluation indicators include a risk degree and a sensitivity.

[0027] (III) Beneficial effects

[0028] The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation provided by the present invention, by statistically analyzing the historical development data of the current batch of wells in the target research area, quantitatively characterizing the key indicators of the target waterflooding oilfield, further obtaining the risk degree range of the production allocation of the target waterflooding oilfield, establishing a risk assessment model for the production allocation of the target waterflooding oilfield, and using the model to analyze and evaluate the risk of the production allocation of the target waterflooding oilfield, can not only scientifically guide the formulation of feasible goals for the target waterflooding oilfield, but also avoid production risks and timely adjust the planning and deployment. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a flowchart of the method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to an embodiment of the present invention;

[0030] Figure 2 is an analysis diagram of waterflooding oilfield planning and production allocation parameters according to an embodiment of the present invention;

[0031] Figure 3 is a process diagram of production random simulation according to an embodiment of the present invention;

[0032] Figure 4 is a production frequency distribution and cumulative probability diagram of a certain planning scheme according to an embodiment of the present invention, wherein, (a) is the production frequency distribution and cumulative probability diagram of the first year, and (b) is the frequency distribution and cumulative probability diagram of the second year;

[0033] Figure 5 is a sensitivity analysis diagram of the production of a certain planning scheme in the second year according to an embodiment of the present invention, wherein, (a) is the sensitivity analysis diagram of the production in the second year Figure 1, (b) is the sensitivity analysis chart of the production in the second year Figure 2 . Specific implementation manners

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Figure 1 is a flowchart of the method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation in the embodiments of the present invention. As Figure 1 shown, the present invention provides a method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation, which specifically includes:

[0036] Step S1: Select a target waterflooding oilfield, analyze the uncertain factors affecting the production scale of the target waterflooding oilfield, and extract key indicators according to the obtained uncertain factors.

[0037] In this method, in step S1, by analyzing the production and production allocation parameters of the target waterflooding oilfield, the uncertain factor is the waterflooding production, and the waterflooding production includes the production of old wells without measures, the production of old wells with measures, and the production of new wells. According to the obtained uncertain factors, key indicators are extracted, and the key indicators include the decline rate of old wells, the annual oil increment of single-well measures, and the daily oil production of new wells per well.

[0038] Figure 2 is an analysis chart of waterflooding oilfield planning and production allocation parameters in the embodiments of the present invention. As Figure 2 shown, in this embodiment, the core of the planning and production allocation plan is production prediction. At present, most oilfields mainly rely on waterflooding. Due to factors such as the deterioration of development targets, the immaturity of development research technologies, and the change of oil prices, there are uncertainties in many development indicators related to the planned production. From the analysis of waterflooding oilfield production planning and production allocation parameters, the factors affecting production composition are various. Generally, the waterflooding production can be divided into three major parts: the production of old wells without measures, the production of old wells with measures, and the production of new wells.

[0039] First, regarding the production without measures, the prediction of waterflooding production without measures is the decline rate of old wells. Under different regions, different geological conditions, and different well patterns and well spacings, the decline rate of old wells is different, resulting in uncertainties in determining the decline rate of old wells. At the same time, the determination of the decline rate of old wells is also affected by factors such as new wells, long-shut wells, injection-production system adjustment, fine tapping potential, drilling shut-off, plugging, and the transfer from waterflooding to polymer flooding.

[0040] Second, regarding the incremental oil production from measures, the production of old wells affected by measures mainly includes two aspects: the annual incremental oil production from measures and the measure potential. Firstly, the annual incremental oil production effect of measures. Different regions and geological conditions result in differences in the effects of measures, leading to uncertainties in determining the measure effects. On the other hand, although various methods can be used to predict the annual incremental oil production effect of measures, due to the uncertain phenomena in the changes of underground rocks, fluid properties, and production dynamics, these parameters have certain uncertainties. Secondly, the measure potential is related to geology, technology, and oil prices. On the premise that geology and technology remain unchanged, the incremental oil production limits corresponding to different oil prices are different. Higher oil prices correspond to lower incremental oil production effect limits, and thus the statistically calculated measure potential is relatively more, and vice versa. Therefore, the uncertainty of oil prices brings uncertainty to the measure potential.

[0041] Thirdly, regarding the oil production of new wells, the main factors affecting the production scale of new wells are deployment capacity, production capacity contribution rate, decline of new wells, and water cut, etc. Practical statistics and existing understanding results show that the daily oil production per well of new wells is the main influencing factor for the production scale of new wells. It can be seen that the uncertain factors affecting the production scale include the decline rate of old wells and related influencing factors, the incremental oil production from measures and related factors, and the oil production of new wells and related factors.

[0042] In this embodiment, based on the above analysis, according to the planned production structure, combined with statistical methods and expert experience, three key indicators affecting production are determined. The first category is the decline rate type of indicators. The core affecting the production scale of un-measured wells is the decline rate of old wells. Other influencing factors have mutual influences and are not significant, so they are ignored. The second category is the incremental oil production from measures type of indicators. For the incremental oil production indicators of water flooding measures, it includes the annual incremental oil production per well from measures and the annual incremental oil production from measures. Considering that the annual incremental oil production from measures is the product of the annual incremental oil production per well from measures and the workload, and the workload generally remains relatively stable, the main influencing factor for the incremental oil production from measures type of indicators is the annual incremental oil production per well from measures. The third category is the oil production of new wells type of indicators. Similar to the incremental oil production from measures type of indicators, the water flooding new well oil production type of indicators includes the annual oil production of new wells and the annual oil production per well of new wells. Considering that the annual oil production indicator of new wells is the product of the daily oil production per well of new wells and the workload, and the workload generally remains relatively stable, the main influencing factor for the oil production of new wells type of indicators is the annual oil production per well of new wells. Therefore, based on the actual situation of the oilfield and empirical understanding, three key indicators affecting the uncertainty factors of the production scale of water flooding oilfields are selected: the decline rate of old wells, the annual incremental oil production per well from measures, and the daily oil production per well of new wells.

[0043] Step S2: Select a target study area according to the target water flooding oilfield determined in the above step S1, statistically analyze the historical development data of the current batch of wells in the target study area, and quantitatively characterize the key indicators obtained in the above step S1.

[0044] In practical applications, in step S2, the regional types of the wells in the current batch in the target study area include basic well patterns, primary well patterns, secondary well patterns, and tertiary well patterns. The time range of historical development data is 5 - 10 years. In this method, the quantitative characteristics of the key indicators include the distribution type function, mean, and variance of the decline rate of old wells; the oil production increase effect of single fracturing wells, the oil production increase effect of single well replacement of three types, the oil production increase effect of single recompletion wells, and the oil production increase effect of other single wells; the daily oil production per well of new wells in old areas, the daily oil production per well of peripheral vertical wells, and the daily oil production per well of peripheral horizontal wells.

[0045] In this method, regarding the principle of the quantitative characterization method, considering that the uncertain indicators in the development plan and production allocation plan are objective, random variables are used to describe the values of the indicators, and their characterization is given by means of mathematical statistics analysis methods.

[0046] On the one hand, for the uncertain indicators with data sample points, there are mainly three cases in its quantification method: the type of the random variable distribution is known, and the parameters of this distribution need to be determined from the observed data; the probability distribution type of the random variable is determined from the observed data, and on this basis, its parameters are determined; it is difficult to determine the theoretical distribution form of the random variable from the existing observed data, then an experimental distribution is defined.

[0047] On the other hand, for the indicators without data sample points, or the number of data samples of the indicators is small and the distribution type cannot be accurately given by quantitative methods, the distribution type and its parameters can be given by experts according to experience.

[0048] In practical applications, regarding the quantitative characterization results, a combination of quantitative and qualitative methods is adopted to quantitatively characterize the determined main control indicators of the plan.

[0049] First, regarding the decline rate of old wells, according to mathematical statistics theory, to accurately give the distribution type of the indicator, a sufficient number of statistical sample numbers are required. The decline rate of the statistically counted old wells is counted according to the branch factory, well pattern, and annual production wells to count the change of the decline rate over the years, and based on all the statistical samples, the distribution state of the decline rate of old wells is determined.

[0050] In this embodiment, for Oilfield A in the eastern part of China, according to four methods of basic well pattern, primary well pattern, secondary well pattern, and tertiary well pattern, taking the production wells of the current year as the starting point, the historical development data of the wells in the current year batch is traced back in the development database, and then the Sipaichev water drive law curve is applied to predict development indicators such as the decline rate of production wells in subsequent years under different well patterns. A two-dimensional matrix of decline rates is obtained, that is, data samples in the time dimension and the numerical dimension of different well patterns and the same well count by year. Using the calculation and analysis method of the uncertainty measure theory, the distribution type function, mean value, and variance of the decline rate are obtained. From the prediction of the natural decline rate of the basic well pattern, from 2013 to 2020, the change range is between 3.1 and 4.7, indicating that the old wells in the basic well pattern have declined to the later stage. From the prediction of the natural decline rate of the primary well pattern, from 2013 to 2020, the change range is between 3.5 and 7.0. From the prediction of the natural decline rate of the secondary well pattern, from 2013 to 2020, the change range is between 5.0 and 15.0. From the prediction of the natural decline rate of the tertiary well pattern, from 2013 to 2020, the change range is between 6.0 and 12.0.

[0051] Therefore, combining expert experience and the research results of the decline rate, using the uncertainty measure theory method and expert experience, the type of the distribution function is comprehensively determined to be a normal function, and the decline rate range is 6 - 8%.

[0052] Second, regarding the measure effect, through "statistical analysis + uncertainty measure theory + expert experience", combined with the changing trend of the quality of the reserves, the type and range of the distribution function are comprehensively determined.

[0053] In this embodiment, regarding the oil increment effect of a single well with measures. From the annual oil increment change curve of fracturing single wells over the years, the fracturing effect in Oil Region A has been decreasing year by year, dropping from 400 - 700 tons / year in 2005 to 300 - 500 tons / year in 2012, and showing a continued downward trend. From the statistical histogram, it has relatively significant normal distribution characteristics. In the same way, the distributions followed by other measures for old wells can be determined. Other measures for old wells include three replacements, perforation repair, and others.

[0054] In this embodiment, regarding the daily oil production of a single new well. The daily oil production of a single new well can be divided into three categories: new wells in the old area, peripheral vertical wells, and peripheral horizontal wells. For the daily oil production of a single water - flooded well in the old area, it is determined by analogy according to geological conditions, adjustable thickness, water - flooding degree, etc.; for the daily oil production of a single new well in the peripheral new area, the peripheral new area new wells include vertical wells and horizontal wells. According to the reserve grade, single - well effective thickness, where the daily oil production of a single well = single - well effective thickness * oil production intensity, and the daily oil production level of a single well in the adjacent developed block, the daily oil production of a single well is qualitatively given.

[0055] Table 1 shows the measures, the effects of new wells, and the distribution type. As shown in Table 1, due to the small number of data samples, it is not easy to determine its distribution type, so it is qualitatively determined to belong to a uniform distribution.

[0056] Table 1 Measures, the effects of new wells, and the distribution type

[0057]

[0058] Step S3: According to the quantitative characterization obtained in the above step S2, calculate the production expectation value of the target waterflooding oilfield under different probability conditions, and obtain the risk degree range of the production allocation output of the target waterflooding oilfield.

[0059] In this method, in step S3, the method for obtaining the risk degree range includes the Monte Carlo random simulation method and the risk variable simulation method. The steps for obtaining the risk degree range are as follows: First, obtain random numbers through the Monte Carlo random simulation method, and then, according to the random number generation method, obtain the risk degree range through the risk variable simulation method.

[0060] In practical applications, the theoretical basis of the Monte Carlo random simulation method is the law of large numbers and the central limit theorem in probability theory; the risk variable simulation method includes the simulation method of risk variables subject to uniform distribution, the simulation method of risk variables subject to normal distribution, and the simulation method of risk variables subject to triangular distribution.

[0061] In this method, in the overall risk assessment of the actual production plan, there are multiple coexisting uncertain factors affecting the production composition, and the variation law of the uncertain factors can often be described by probability distribution. Then, the risk assessment of the production plan is to, on the basis of probability estimation of the uncertain factors for the random variable, reflect the risk degree of the production plan by calculating statistical indicators such as the production expectation value under different probability conditions. The commonly used method is the Monte Carlo random simulation method.

[0062] In practical applications, the Monte Carlo simulation, also known as the statistical test method, is a simulation technique that samples each random variable, substitutes it into the data model, and determines the function value. Among them, independent simulation tests are carried out N times to obtain a set of sampling data of the function, from which the distribution type, expectation, variance and other probability distribution characteristics of the function can be determined, and then its risk degree can be obtained. The theoretical basis of the Monte Carlo method is the law of large numbers and the central limit theorem in probability theory.

[0063] Among them, the law of large numbers reflects the property of the sum of a large number of random numbers, that is, the mean value of the random numbers converges to the function expectation value; the central limit theorem means that regardless of the distribution of a single random variable, the sum of multiple independent random variables follows a normal distribution. Based on these two theorems, the basic principle of the Monte Carlo method can be described as follows:

[0064] Suppose the function of random variables where X1, X2, X n are n mutually independent random variables, and each has a certain probability distribution; use a random number generator to extract each set of random variables X1, X2, X n by direct or indirect sampling Then, according to the relationship between Y and X1, X2, X n determine the value of the function Y Repeatedly and independently sample or simulate N times, and a batch of sampling data y1, y2, y N of the function Y can be obtained. This batch of numbers conforms to the characteristics of the normal distribution; when the number of simulations N→∞, the probability distribution and its numerical characteristics of the function Y close to the actual situation can be given.

[0065] In practical applications, the key to Monte Carlo simulation is to generate high-quality random numbers. In the simulation, we need to generate random numbers of various probability distributions, and the generation of random numbers of most probability distributions is based on the random numbers of the uniform distribution U(0,1). Among them, the generation method of U(0,1) random numbers uses the multiplicative congruential method:

[0066]

[0067] where α, m, and x n are all integers. α is the multiplier coefficient. First, the remainder of αx n divided by m is assigned to x n+1 to generate a sequence of {x n} by the method of analogy, where the seed x0 is a variable number given by us; then, through r n+1 =x n+1 / m to generate the numbers of the sequence of {r n}. Since x n is the remainder in the division with the divisor m, so 0≤x n ≤m, 0≤r n ≤1. It can be seen that the sequence of {r n} is a sequence of uniformly distributed pseudo-random numbers on (0,1).

[0068] In this method, through the analysis and verification of a large number of actual statistical data of the oilfield, according to the random number generation method, according to the equation F(x n ) = r n find the relationship between x n and r n x n =f(r n ). There are the following three common simulations of risk variables with special distributions.

[0069] First, the simulation method for risk variables subject to a uniform distribution:

[0070] The distribution function is:

[0071] In the formula, a and b are the estimated values of the lower and upper limits of the interval respectively.

[0072] The simulation relationship is as follows:

[0073] x n = a + (b - a)·r n .

[0074] Second, the simulation method for risk variables subject to a normal distribution:

[0075] If r1 and r2 are two independent random variables uniformly distributed in the interval (0, 1), and then the uniformly distributed random numbers are converted into normally distributed random numbers. The Box Muller method is often used to obtain a pair of random variables x1 and x2 subject to the standard normal distribution:

[0076]

[0077]

[0078] Among them, for the sampling of the general normal distribution density function N(μ, σ 2 ), the sampling result can be expressed as:

[0079] Y = μ + σ·X

[0080] If the estimated values of the lower and upper limits of the interval of the risk variable subject to the normal distribution are a and b, then there is an approximate relationship:

[0081]

[0082] Third, the simulation method for risk variables subject to a triangular distribution

[0083] The distribution function is:

[0084] In the formula, a, b, and c are the estimated values of the lowest, highest, and most likely respectively.

[0085] Its simulation relationship is as follows:

[0086]

[0087] Step S4: According to the risk degree range obtained in the said step S3, establish a risk assessment model for the production allocation output of the target waterflooding oilfield.

[0088] In practical applications, probability analysis is mainly carried out on the determined production rate of the plan. The production rate of the plan can be manually arranged or optimized, and the risks generated when various uncertain factors affecting production change are studied.

[0089] In this method, a method combining qualitative and quantitative analysis and human-computer interaction is applied. Based on the measure effect knowledge base, a risk assessment model for the production rate of the plan is established. The distribution types of uncertain factors are given using historical data or expert experience, such as the decline rate and the effect of oil-increasing measures. Uncertainty simulation technology is used to analyze the uncertain production corresponding to the development plan, and its probability density function and cumulative distribution function are given, so as to make an accurate judgment on the risk of the production plan.

[0090] In this method, regarding the expression of the production simulation function, in order to quantify the uncertainty of the production volume completed by the plan and the risk of the plan achieving the specified goal, Monte Carlo simulation technology is used for quantitative analysis, that is, the probability distribution of the production volume Q 水驱 (t) and the risk of completing the production volume are given.

[0091] In practical applications, in step S4, the model calculation formula of the risk assessment model is the production simulation function expression, and its expression is:

[0092]

[0093] In the formula:

[0094] —— represents the increased oil production of all measures and new wells each year;

[0095] —— represents the declining production of old wells each year;

[0096] Q hx (t)—— the aftereffect production of new wells in the t-th year at the beginning of the plan.

[0097] In this embodiment, regarding the production random simulation process, the simulation idea is as follows: According to the distribution type and change interval range of each uncertain factor affecting production, using Monte Carlo simulation technology, a certain number of samples are randomly selected, and the frequency histogram and probability cumulative distribution diagram of the oil production for the planned plan are given; According to the probability cumulative distribution diagram, the probability of the planned production plan completing the task and the production volume under the acceptable risk level can be obtained, which are provided to the decision maker as a basis for making decisions.

[0098] Figure 3 For the production random simulation process diagram of the embodiment of the present invention, as Figure 3As shown, in practical applications, considering the variation range of the upper and lower limits of the workload of various measures during the planning period, to ensure the relative balance of randomly sampled sample points within the workload range, when the computing time permits, the simulation parameter values should be made as large as possible.

[0099] Step S5: According to the risk assessment model in step S4, conduct a risk analysis and evaluation of the production allocation output of the target waterflooding oilfield, and reasonably predict the output completion task of the target waterflooding oilfield next time.

[0100] In practical applications, in step S5, the evaluation methods for risk analysis and evaluation include the risk estimation method, the frequency analysis method, and risk assessment indicators. Among them, the risk assessment indicators include the risk degree and the sensitivity.

[0101] In this method, mainly relying on historical statistical data, a mathematical model is constructed using mathematical methods for evaluation. According to the probability distribution characteristics of the influencing factors, a risk analysis and evaluation of the production plan is realized. The specific introduction of the analysis methods is as follows:

[0102] First, regarding the risk estimation method, according to the risk characteristics and types, certain mathematical tools are used to measure or estimate the risk magnitude. The commonly used methods mainly include the subjective estimation method, the objective estimation method, the expected value method, the mathematical model method, the stochastic simulation method, and the Markov model method, etc. Here, the expected value analysis is adopted.

[0103] Among them, the expected value E(x), also known as the mean, reflects the average value of the results when the random variable occurs repeatedly. It refers to the average value obtained by weighting several possible consequences of a random variable with their respective probabilities. From the perspective of risk measurement, the expected value of the output reflects the completion situation of the output target task. The degree of deviation between the output task completion situation and the target reflects the risk degree of this production plan. The greater this deviation, the greater the risk degree of the production plan. In practical applications, the larger the output expected value E(x), the better.

[0104] Second, regarding the frequency analysis method, the frequency analysis method is a method based on the calculation of the probability of a certain accident occurring. Usually, the probability of the output occurrence frequency is described to determine the possible frequency of the completion output risk occurring, or to predict the probability of future occurrence, so as to obtain the most likely interval representing the output completion, and the changing law of the total risk of the output target completion can be seen from the frequency curve. For example, the probability of output completion can be observed through the output cumulative probability distribution graph, and the probability value can reflect the risk degree of the plan. The closer this probability value is to 1, the greater the risk of the plan; on the contrary, the smaller the risk of the plan.

[0105] Thirdly, regarding the risk assessment indicators, among which the risk degree is an important evaluation indicator. It is an indicator that describes the degree of dispersion of a variable deviating from the expected value. The greater the risk degree, the less certain the decision-maker is about the future completion of the production plan, and the greater the risk; conversely, the smaller the risk degree.

[0106] In practical applications, the risk degree R D is generally jointly measured by the standard deviation S(x) and the mean E(x):

[0107]

[0108] When E(x) is constant, the greater the standard deviation S(x), the greater the degree of dispersion of the relevant numerical distribution, which means the greater the risk contained in the production plan; the smaller S(x), the closer the distribution of various possible values is to the expected value, and the actual occurrence number will be closer to the expected value, which means the smaller the risk contained in the production plan.

[0109] In practical applications, when the standard deviations are the same, the smaller the expected value, the greater the risk degree; the greater the risk degree, the less certain the decision-maker is about the future completion of the production, and the greater the risk; conversely, the smaller the risk degree; usually, the size of the standard deviation of the production plan is regarded as a specific indicator of the size of the risk it contains.

[0110] In this embodiment, by using the Crystal Ball risk analysis tool to conduct a production probability analysis on the most likely plan of a certain waterflooding plan in Oilfield A, indicators such as the probability of annual production completion, the production change range, and the risk degree of the plan can be obtained.

[0111] In practical applications, annual probability analysis needs to be carried out. Table 2 is the probability analysis result table of the most likely plan of a certain planning plan. As shown in Table 2 and Figure 4 shown, from the annual simulation results, the risk degrees of each year of the plan are relatively small, and the possibility of achieving the production target of the plan is high. For example, if the probability of a high-probability event is set at 90%, at the same time, the greater the size of the annual production expected value, the production change range, and the degree of dispersion of the production distribution, the greater the uncertainty and the greater the risk, so more attention should be paid.

[0112] Table 2 Probability analysis result table of the most likely plan of a certain planning plan

[0113]

[0114] In addition, sensitivity analysis also needs to be carried out. By analyzing the comprehensive influence degree of uncertain factors on production, the annual decline rate has the greatest sensitivity to the current year's production, and the daily oil production per well of new wells has the strongest sensitivity to the production in the second year. For example Figure 5As shown, therefore, through the sensitivity analysis of uncertainty factors, the indicators with strong sensitivity can be adjusted and monitored to achieve a more accurate prediction of the completion of production tasks, so as to meet the requirements of different decision-making purposes.

[0115] The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation provided by the present invention quantifies and characterizes the key indicators of the target waterflooding oilfield by statistically analyzing the historical development data of the current-year batch wells in the target research area, further obtains the risk degree range of the production allocation output of the target waterflooding oilfield, establishes a risk assessment model for the production allocation output of the target waterflooding oilfield, and uses the model to analyze and evaluate the risk of the production allocation output of the target waterflooding oilfield. It can not only scientifically guide the formulation of feasible goals for the target waterflooding oilfield, but also avoid production risks and timely adjust the planning and deployment.

[0116] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those of ordinary skill in the relevant technical fields can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention. The patent protection scope of the present invention shall be defined by the claims.

Claims

1. A method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation, characterized in that, Including: Step S1: Select a target waterflooding oilfield, analyze the uncertainty factors affecting the production scale of the target waterflooding oilfield, and extract key indicators based on the obtained uncertainty factors. Step S2: Select a target research area according to the target waterflooding oilfield determined in Step S1, count the historical development data of the current-year batch wells in the target research area, and quantitatively characterize the key indicators obtained in Step S1. Step S3: Calculate the production expectation values of the target waterflooding oilfield under different probability conditions based on the quantitative characterization obtained in Step S2, and obtain the risk degree range of the allocated production of the target waterflooding oilfield. Step S4: Establish a risk assessment model for the allocated production of the target waterflooding oilfield according to the risk degree range obtained in Step S3. Step S5: Conduct a risk analysis and evaluation of the allocated production of the target waterflooding oilfield according to the risk assessment model in Step S4, and reasonably predict the completion of the production task of the target waterflooding oilfield next time.

2. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 1, characterized in that In Step S1, by analyzing the production and allocation parameters of the target waterflooding oilfield, the uncertainty factor is the waterflooding production, and the waterflooding production includes the production of old wells without measures, the production of old wells with measures, and the production of new wells.

3. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 1, wherein In Step S1, the key indicators include the decline rate of old wells, the annual oil increment of single-well measures, and the daily oil production of new wells per well.

4. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 1, characterized in that, In Step S2, the regional types of the current-year batch wells in the target research area include the basic well pattern, the first well pattern, the second well pattern, and the third well pattern, and the time range of the historical development data is 5 - 10 years.

5. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 1, characterized in that, In Step S2, the quantitative characteristics of the key indicators include the distribution type function, mean value, and variance of the decline rate of old wells; the oil increment effect of fracturing single wells, the oil increment effect of three-replacement single wells, the oil increment effect of perforation-completion single wells, and the oil increment effect of other single wells; the daily oil production of new wells in the old area per well, the daily oil production of peripheral vertical wells per well, and the daily oil production of peripheral horizontal wells per well.

6. The method for quantitatively determining the completion probability of water flooding oilfield development and production allocation according to claim 1, characterized in that, In Step S3, the method for obtaining the risk degree range includes the Monte Carlo random simulation method and the risk variable simulation method. The steps for obtaining the risk degree range are as follows: first, obtain random numbers through the Monte Carlo random simulation method, and then obtain the risk degree range through the risk variable simulation method according to the random number generation method.

7. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 6, wherein The theoretical basis of the Monte Carlo random simulation method is the law of large numbers and the central limit theorem in probability theory; the risk variable simulation method includes the simulation method of risk variables subject to uniform distribution, the simulation method of risk variables subject to normal distribution, and the simulation method of risk variables subject to triangular distribution.

8. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 1, characterized in that In Step S4, the model calculation formula of the risk assessment model is the production simulation function expression, and its expression is: Where: —— Representing all measures per year and the increased oil production of new wells; —— indicating the annual decline in production of old wells; Q hx (t) —— The aftereffect production of the new well at the beginning of the planning in the t-th year.

9. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 1, characterized in that, In Step S5, the evaluation methods for the risk analysis and evaluation include the risk estimation method, the frequency analysis method, and the risk assessment index.

10. The method for quantitatively determining the completion probability of waterflooding oilfield development and production allocation according to claim 9, characterized in that, The risk assessment index includes the risk degree and the sensitivity.

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