Analysis method for determining operating cost level difference of developed water-drive oil field

Abstract

The invention relates to an analysis method for determining the operating cost level difference of a developed water-drive oil field. The method mainly solves the problem that subjectivity and limitation are high when an existing oil field operation cost level difference is determined. The method is characterized by comprising the following steps: S1, determining all levels of factors influencing the operation cost; s2, determining the weight and the total weight of each level of factor; s3, determining characteristic values of all levels of factors; and S4, determining a cost level difference calculation model. According to the method, the operating cost level difference of the developed water-drive oil field can be determined through multi-level analysis, factors are considered more comprehensively, the analysis method is more objective, and the result is more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and specifically to an analysis method for determining the operation cost differential of developed waterflood oilfields. Background Art

[0002] In addition to being widely used in the evaluation of oil and gas reserve value and asset evaluation, the cost differential method can also be used to rank and compare the operation costs of developed oilfields or blocks, and to allocate the operation costs of oilfields according to the cost differential. As an underground natural resource, the exploitation of oil is affected by many factors such as natural environment, resource conditions, and development technology policies. Due to the differences in the above conditions, there are obvious differences in the crude oil costs of different development units in oilfields. The research on the cost changes over the years shows that there are the following two problems: First, the problem of analyzing and comparing the changing trends of operation costs. Under the premise of the same influencing factors, the influencing degrees and roles of the operation costs of different oilfields or block units are not the same, that is, it is impossible to analyze and describe the cost changes of different blocks in the whole oilfield with a certain specific trend; Second, the problem of determining the allocation of operation costs for different development units. Generally, the production averaging method is used for equal sharing, which has a certain rationality, but it also does not consider the problem of the operation cost differences between development units, and has great limitations. The original cost differential determination methods mostly use the Delphi method or the linear regression method to determine, and their subjectivity or restrictiveness is relatively strong. Summary of the Invention

[0003] In order to overcome the deficiencies of strong subjectivity and restrictiveness in the determination of the existing oilfield operation cost differential, the present invention provides an analysis method for determining the operation cost differential of developed waterflood oilfields. This method can determine the operation cost differential of developed waterflood oilfields through multi-level analysis, considering more comprehensive factors, more objective analysis methods, and more accurate results.

[0004] The technical solution of the present invention is: an analysis method for determining the operation cost differential of developed waterflood oilfields, including:

[0005] S1. Determine the factors at all levels that affect the operation cost;

[0006] S2. Determine the weight values and total weight values of the factors at all levels;

[0007] S3. Determine the characteristic values of the factors at all levels;

[0008] S4. Determine the cost differential calculation model:

[0009]

[0010] In the formula: F CB - Total cost differential value;

[0011] W ij - Total weight value of factors;

[0012] f ijk - Eigenvalues of different levels of factors;

[0013] N - Number of primary factors;

[0014] M(i) - Number of secondary factors of the i-th primary factor;

[0015] P(j) - Number of levels of the j-th secondary factor;

[0016] β - Cost adjustment coefficient, taking 1.0.

[0017] Furthermore, the total weight value W of the factors in step S4 ij is:

[0018] W ij = W i ·W j

[0019] In the formula: W i - Weight value of primary factors, W j - Weight value of secondary factors in primary factors.

[0020] Furthermore, the eigenvalue f of different levels of the factors in step S4 ijk is:

[0021] f ijk = (f ik + f jk ) / 2

[0022] In the formula: f ik - Eigenvalue of different levels of factors determined by the statistical method; f jk - Eigenvalue of different levels of factors determined by the regression method.

[0023] Furthermore, the statistical method is: classifying a certain type of data, calculating the arithmetic mean of the classification results, determining the initial reference eigenvalue, and comparing the remaining values with the initial reference eigenvalue to obtain the corresponding eigenvalues.

[0024] Furthermore, the regression method is: statistically regressing a certain type of data, establishing a mathematical calculation formula, calculating the average value after classification according to the regression formula, determining the initial reference eigenvalue, and comparing the remaining values with the initial reference eigenvalue to obtain the corresponding eigenvalues.

[0025] Furthermore, the primary factors affecting operation costs include geological factors, development factors, and natural condition factors. Among them, the secondary factors in geological factors include reservoir burial depth, reserve abundance, and permeability; the secondary factors in development factors include well pattern density, daily oil production per well, daily liquid production per well, and daily water injection per well; the secondary factors in natural condition factors include distance and geomorphology.

[0026] Furthermore, the weight W of the primary factors in step S2 i is as follows: the weight of geological factors is 0.5402, the weight of development factors is 0.3478, and the weight of natural conditions is 0.112.

[0027] Furthermore, the weight W of the secondary factors in step S2 j is as follows: among the secondary factors of geological factors, the weight of reserve abundance is 0.4846, the weight of reservoir burial depth is 0.3899, and the weight of permeability is 0.1255; among the secondary factors of development factors, the weight of well pattern density is 0.2432, the weight of daily oil production per well is 0.2949, the weight of daily liquid production per well is 0.3438, and the weight of daily water injection per well is 0.1181; among the secondary factors of natural condition factors, the weight of distance is 0.25, and the weight of geomorphology is 0.75.

[0028] An application of the operation cost differential of a developed waterflooded oilfield, which calculates the block cost differential using a cost differential calculation model, selects a reference comparison block, and calculates the reference cost value of the block to be compared according to the following formula:

[0029]

[0030] In the formula: CB i —— The crude oil cost to be determined for the i-th block or oilfield;

[0031] CB0 —— The crude oil cost of the comparison block or oilfield;

[0032] —— The cost differential coefficient of the i-th block or oilfield;

[0033] —— The cost differential coefficient of the comparison block or oilfield.

[0034] Furthermore, according to the total operation cost of the entire oilfield and the waterflood well production composition of each combined unit, combined with the determined cost differential, the unit operation cost of each combined unit is allocated;

[0035] (1) The total operation cost of the oilfield is:

[0036]

[0037] In the formula: CB —— The total operation cost of the oilfield;

[0038] CB k —— Benchmark cost of the oilfield;

[0039] —— Cost differential;

[0040] Q i —— Oil production of the i-th combined unit;

[0041] —— Cost differential of the i-th combined unit;

[0042] n—— Number of combined units;

[0043] (2) The unit operating cost CB of the benchmark unit k is:

[0044]

[0045] (3) Calculate the unit operating cost of each combined unit according to formula (4).

[0046] The present invention has the following beneficial effects: By adopting the above solution, the method for determining the cost differential based on the multi-level analytic hierarchy process proposed by the present invention conducts multi-level analysis, considers more comprehensive factors, greatly reduces subjectivity, reduces restrictions, and fully considers the operation cost differences between development units. It mainly solves the following three aspects of problems: 1. Select factors that can be quantitatively described for analysis and statistics, and preferentially select factors with strong correlation or factors with offsetting interactions; 2. Combine statistical law analysis and hierarchical and interval analysis to reduce deviations caused by human factors and statistical accuracy; 3. When conducting interval statistics, adopt the "scoring method" to avoid the influence of abnormal points on the statistical results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is the flow chart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] The present invention will be further described below with reference to the accompanying drawings:

[0049] As shown by Figure 1 an analysis method for determining the operation cost differential of a developed waterflooded oilfield includes:

[0050] S1. Determine the factors at all levels that affect the operating cost. Comparative analysis of the crude oil operating costs of 53 waterflooding blocks in Oilfield A in the eastern part of China shows that the cost items with relatively large impacts on the operating cost are material cost, power cost, oil displacement injection cost, downhole operation cost, oil and gas treatment cost, and factory and mine management cost. During the "10th Five-Year Plan" period of this oilfield, the oil displacement injection cost accounted for 29.1% of the operating cost, the factory and mine management accounted for 13.11%, the power cost accounted for 12.1%, and the material and downhole operation costs accounted for 8.68% and 9.59% respectively, together accounting for 72.58% of the operating cost. It can be seen that the above cost items are the main reasons for the change in cost. The factors affecting their changes are mainly three aspects: geological factors, development factors, and natural environment factors of the oilfield. Among them, the secondary factors in geological factors include reservoir burial depth, reserve abundance, and permeability; the secondary factors in development factors include well pattern density, daily oil production per well, daily liquid production per well, and daily water injection per well; the secondary factors in natural condition factors include distance and landform, as shown in Table 1 for details.

[0051] Table 1 Classification Table of Cost Differential Factors

[0052]

[0053] In the table, F1, F2, F3, and F 11 , F 12 , …, F 32 are the primary and secondary factors respectively. These factors have their own weight values, and these weight values can be determined by using the analytic hierarchy process according to the magnitude of the influence of the factors on the crude oil cost in different classifications. The parameters in the factor level (also known as the characteristic values of the factors) are determined by using the statistical analysis method of the factors.

[0054] S2. Determine the weight values and total weight values of the factors at all levels. The total factor weight value W ij is:

[0055] W ij = W i ·W j (1)

[0056] In the formula: W i - weight value of the primary factor, W j - weight value of the secondary factor in the primary factor.

[0057] (1). Determination of the primary weight value. According to the influence degree of different factors on the operating cost, construct the comparative discrimination matrix of the primary influence factors, as shown in Table 2.

[0058] Table 2 Comparative Discrimination Matrix of Primary Factors

[0059]

[0060] The maximum eigenvalue λ max= 3.0013, the consistency convergence coefficient CR is 0.0011, which is less than the consistency convergence discriminant value of 0.10. It can be seen that the discriminant matrix has satisfactory consistency.

[0061] (2) Determination of secondary weights. The secondary weights include three parts, namely the weights of factors related to geological factors, the weights of factors related to development factors, and the weights of factors related to natural conditions.

[0062] ① Determination of weights of factors related to geological factors. The comparison discriminant matrix of structural geological influence factors is shown in Table 3.

[0063] Table 3 Comparison discriminant matrix of geological factors

[0064]

[0065]

[0066] The maximum eigenvalue λ of the comparison discriminant matrix of the structure max = 3.0012, the consistency convergence coefficient CR = 0.001063, which is less than the consistency convergence standard of 0.10. The discriminant matrix has satisfactory consistency.

[0067] ② Determination of weights of factors related to development factors. The comparison discriminant matrix of structural development influence factors is shown in Table 4.

[0068] Table 4 Comparison discriminant matrix of development factors

[0069]

[0070] The maximum eigenvalue λ of the comparison discriminant matrix of the structure max = 4.0017, the consistency convergence coefficient CR is 0.00065, which is much less than the discriminant standard of 0.10. It can be seen that the discriminant matrix has satisfactory consistency.

[0071] ③ Determination of weights of factors related to natural conditions. The comparison discriminant matrix of structural development influence factors is shown in Table 5.

[0072] Table 5 Comparison discriminant matrix of natural condition factors

[0073]

[0074] (3) Determination of the total weight. According to formula (1), combined with Table 2 and Tables 3, 4, and 5, the total weights of each factor can be obtained. See Table 6 for details.

[0075] Table 6 Final result table of weight determination

[0076]

[0077]

[0078] After calculation, the consistency convergence coefficient CR = 0.000834, and the discrimination matrix has satisfactory consistency.

[0079] S3. Determine the eigenvalues of each level of factors. To reduce the error of determining eigenvalues by a single method, the eigenvalues of factors are determined by two methods and then averaged. Therefore, the eigenvalues f of different levels of factors ijk are as follows:

[0080] f ijk =(f ik +f jk ) / 2 (2)

[0081] In the formula: f ik - The eigenvalues of different levels of factors determined by the statistical method; f jk - The eigenvalues of different levels of factors determined by the regression method.

[0082] Among them, the statistical method is as follows: First, classify a certain type of data; second, calculate the arithmetic mean of the classification results; finally, determine the initial reference eigenvalue, and compare the remaining values with the reference eigenvalue to obtain their corresponding eigenvalues.

[0083] The regression method is as follows: First, establish a mathematical calculation formula by statistical regression for a certain type of data; second, calculate the average value after classification according to the regression formula; finally, determine the reference eigenvalue, and compare the remaining values with the reference eigenvalue to obtain the corresponding eigenvalues.

[0084] (1). Geological factors, including three aspects: reservoir burial depth, reserve abundance, and permeability.

[0085] ① Reservoir burial depth. The deeper the oil layer, the more complex the oil production technology, the greater the workload required to maintain production, resulting in a high material cost per well. Due to the increase in well depth, the lifting distance of unit liquid from the bottom of the well to the wellhead is also increased, and the power cost consumed for crude oil production is increased, causing an increase in the cost of crude oil.

[0086] The eigenvalue determined by the statistical method is: Classify according to the oil layer depth, calculate the average value of the crude oil cost at each level, determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost at the remaining levels with the initial reference eigenvalue to obtain the corresponding eigenvalues. See Table 7 for details.

[0087] The eigenvalue determined by the regression method is: Establish a regression curve formula according to the original data of the oil layer depth:

[0088]

[0089] In the formula, H - reservoir burial depth, m;

[0090] CB i—— Crude oil cost, yuan / t;

[0091] Calculate the average value of the crude oil cost after classification according to the regression curve formula; determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost of each other level with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 7 for details.

[0092] Table 7 Statistical analysis results of the relationship between reservoir burial depth and crude oil cost

[0093]

[0094] Calculate the eigenvalue of the reservoir burial depth according to formula (2), see Table 8 for details.

[0095] Table 8 Eigenvalues of reservoir burial depth

[0096]

[0097] ② Reserve abundance. The statistical results show that generally, the larger the reserve abundance, the relatively better the geological conditions and the lower the cost per unit reserve; the smaller the reserve abundance, the poorer the reservoir conditions and the higher the cost per unit reserve.

[0098] Similarly, the eigenvalue determined by the statistical method is: classify according to the reserve abundance, count the average value of the crude oil cost in each level, determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost of each other level with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 9 for details.

[0099] The eigenvalue determined by the regression method is: establish a regression curve formula based on the original data of the reserve abundance:

[0100] C i =12.472I o -0.331

[0101] where I o —— Reserve abundance, 10 4 t / km 2 ;

[0102] C i —— Cost per unit reserve, yuan / t;

[0103] Calculate the average value of the crude oil cost after classification according to the regression curve formula; determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost of each other level with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 9 for details.

[0104] Table 9 Statistical analysis results of the relationship between reserve abundance and cost per unit reserve

[0105]

[0106] Calculate the characteristic value of reserve abundance according to formula (2), as shown in Table 10.

[0107] Table 10 Characteristic values of reserve abundance

[0108]

[0109]

[0110] ③ Permeability. The statistical results show that the permeability is inversely proportional to the unit operation cost. The lower the permeability of the oilfield, the higher its unit operation cost. Oilfields with high permeability have high liquid production capacity, simple oil production technology, and relatively low costs such as per-ton oil materials consumed.

[0111] The characteristic value determined by the statistical method is as follows: Classify according to permeability, statistically calculate the average value of crude oil costs at each level, determine the initial reference characteristic value as 1, and compare the average value of crude oil costs at the remaining levels with the initial reference characteristic value to obtain the corresponding characteristic values. See Table 11 for details.

[0112] The characteristic value determined by the regression method is as follows: Establish a regression curve formula based on the original permeability data:

[0113] CB i =810.95K -0.201

[0114] where K——average permeability, md

[0115] CB i ——crude oil cost, yuan / t.

[0116] Calculate the average value of crude oil costs after classification according to the regression curve formula; determine the initial reference characteristic value as 1, and compare the average value of crude oil costs at the remaining levels with the initial reference characteristic value to obtain the corresponding characteristic values. See Table 11 for details.

[0117] Table 11 Statistical analysis results of the relationship between permeability and unit operation cost

[0118]

[0119] The characteristic values of permeability calculated according to formula (2) are shown in Table 12.

[0120] Table 12 Characteristic values of average permeability

[0121]

[0122]

[0123] (2) Development condition factors

[0124] There are many development factors affecting the cost of crude oil, such as the oil production method, the oil production index per meter, the viscosity of crude oil, the mobility, the well pattern density, the daily oil production of a single well, etc. However, it is difficult to determine and quantify the magnitude of the impact of each factor on the cost. Among them, some factors such as reservoir type and drive type are often mixed, and the parameters are difficult to quantify. It is even more difficult to formulate a weight value suitable for different oilfields. Therefore, comprehensive indicators are used to replace the difficult-to-quantify and overly trivial indicators.

[0125] ① Well pattern density. The size of the well pattern density directly affects the size of the factory and mine management costs in the cost of crude oil. For relatively large well pattern densities, the management level is high and the management costs are relatively low, and they are generally distributed in the old areas; for relatively small well pattern densities, the management level is low and the invested management costs are relatively high, and such blocks are generally distributed in peripheral oilfields.

[0126] The eigenvalue determined by the statistical method is as follows: Classify according to the well pattern density, and statistically calculate the average value of the crude oil cost at each level. Determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost at the remaining levels with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 13 for details.

[0127] The eigenvalue determined by the regression method is as follows: Establish a regression curve formula based on the original data of the well pattern density:

[0128]

[0129] In the formula, S - well pattern density, wells / km 2 ;

[0130] CB i - Crude oil cost, yuan / t;

[0131] Calculate the average value of the crude oil cost after classification according to the regression curve formula; determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost at the remaining levels with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 13 for details.

[0132] Table 13 Statistical analysis results of the relationship between well pattern density and cost per ton of oil

[0133]

[0134] The eigenvalues of the well pattern density calculated according to formula (2) are shown in Table 14.

[0135] Table 14 Well pattern density eigenvalues

[0136]

[0137] ② Daily oil production of a single well. The daily oil production of a single well is inversely proportional to the unit operating cost. The higher the daily oil production of a single well, the lower the unit operating cost. Conversely, the lower the daily oil production of a single well, the higher the unit cost.

[0138] The eigenvalue determined by the statistical method is as follows: Classify according to the daily oil production per well, and count the average value of the crude oil cost at each level. Determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost at the remaining levels with the initial reference eigenvalue to obtain the corresponding eigenvalues. See Table 15 for details.

[0139] The eigenvalue determined by the regression method is as follows: Establish a regression curve formula based on the regression of the original data of the daily oil production per well:

[0140] CB i = 681.6515R o -0.6666

[0141] In the formula, R o —— Daily oil production per well, t / d;

[0142] CB i —— Unit operating cost of crude oil, yuan / t;

[0143] Calculate the average value of the crude oil cost after classification according to the regression curve formula; determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost at the remaining levels with the initial reference eigenvalue to obtain the corresponding eigenvalues. See Table 15 for details.

[0144] Table 15 Statistical analysis results of the relationship between daily oil production per well and cost

[0145]

[0146] Calculate the eigenvalue of the daily oil production per well according to formula (2), as shown in Table 16.

[0147] Table 16 Eigenvalues of daily oil production per well

[0148]

[0149] ③ The average daily liquid production per well, and the water cut shows an increasing trend with the extension of the oilfield development period. This is the basic law of oilfield development, and this law determines the rapid growth of liquid production and the increase of crude oil cost. Judging from the statistical results of the block, the water cut in the old areas basically exceeds 80%, and nearly 30% of the blocks have a water cut exceeding 90%. On the premise of ensuring production, more work needs to be completed, which leads to a substantial increase in downhole operation costs and oil and gas treatment costs. At the same time, in order to ensure a certain liquid-lifting volume, measures such as replacing small pumps with large pumps are adopted, which also results in an increase in the power cost per ton of oil consumed.

[0150] The eigenvalue determined by the statistical method is as follows: Classify according to the average daily liquid production per well, and count the average value of the crude oil cost at each level. Determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost at the remaining levels with the initial reference eigenvalue to obtain the corresponding eigenvalues. See Table 17 for details.

[0151] The eigenvalue is determined by the regression method: According to the original data of the average daily liquid production per well, a regression curve formula is established by regression:

[0152]

[0153] In the formula, R L ——Average daily liquid production per well, t / d;

[0154] CB y ——Cost per ton of liquid, yuan / t;

[0155] Calculate the average value of the crude oil cost after classification according to the regression curve formula; determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost of each other level with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 17 for details.

[0156] Table 17 Statistical analysis results of average daily liquid production per well and cost per ton of liquid

[0157]

[0158] Calculate the eigenvalue of the daily liquid production per well according to formula (2), as shown in Table 18.

[0159] Table 18 Eigenvalues of daily liquid production per well

[0160]

[0161] ④Average daily water injection per well. Due to the influence of the water injection capacity and system efficiency of different oilfields or blocks, the unit water injection cost is inversely proportional to the average daily water injection volume per well. It can be seen from the statistical blocks that the average daily water injection per well in the old oilfield block is 100.71 m³, and the water injection cost per well is 19.48 yuan / m³; the average daily water injection per well in the peripheral oilfield block is 24.65 m³, and the water injection cost per well is 106.14 yuan / m³. Therefore, the influence degree of the peripheral oilfield by the daily water injection volume per well is significantly higher than that of the old area.

[0162] The eigenvalue is determined by the statistical method: Classify according to the average daily water injection volume per well, count the average value of the crude oil cost in each level, determine the initial reference eigenvalue as 1, and compare the average value of the crude oil cost of each other level with the initial reference eigenvalue to obtain the corresponding eigenvalue. See Table 19 for details.

[0163] The eigenvalue is determined by the regression method: According to the original data, a regression curve formula is established by regression:

[0164] CB z = 1994R z -0.9804

[0165] In the formula, R Z——Average daily water injection volume per single well, m 3 / d;

[0166] CB Z ——Unit water injection cost, yuan / m 3 ;

[0167] Calculate the average value of crude oil cost after classification according to the regression curve formula; determine the initial reference eigenvalue as 1, and compare the average value of crude oil cost at each level with the initial reference eigenvalue to obtain the corresponding eigenvalues. See Table 19 for details.

[0168] Table 19 Statistical results of average daily water injection per single well and unit water injection cost

[0169]

[0170] Calculate the eigenvalue of average daily water injection per single well according to formula (2), see Table 20.

[0171] Table 20 Eigenvalues of daily water injection

[0172]

[0173] (3) Natural condition factors. Natural conditions mainly refer to two aspects: geographical environment and distance.

[0174] ① Geographical environment mainly refers to the landform situation (low-lying well area) where the crude oil production site is located and the soil property situation (the acidity and alkalinity of the soil affect the corrosion of the pipeline network), which mainly affects the maintenance and repair costs of the cost and the consumed material costs. According to experience, generally three levels of good, better, and bad are taken, and the characteristic weight values are taken as 1, 1.2, and 2.4 respectively. In addition, the normalized value of the average elevation of the oil production plant can be used as the eigenvalue of the geographical environment.

[0175] ② Distance can have two interpretations: one is the distance from the block to the reference center, the farther the distance, the greater the cost; the other is the ratio of the oilfield management area to the oil-bearing area, the larger the ratio, the higher the cost. No classification is carried out here, and the normalized value of the ratio of each factory is used as the eigenvalue of the ratio.

[0176] In summary, the eigenvalues of each level of factors are shown in Table 21.

[0177] Table 21 Comparison table of cost differential parameters

[0178]

[0179]

[0180] S4. Determine the cost differential calculation model. From the above statistical analysis, the cost differential calculation model is obtained as:

[0181]

[0182] In the formula: F CB - Total cost level difference;

[0183] W ij - Total factor weight;

[0184] f ijk - Eigenvalues of different factor levels;

[0185] N - Number of primary factors;

[0186] M(i) - Number of secondary factors of the i-th primary factor;

[0187] P(j) - Number of levels of the j-th secondary factor;

[0188] β - Cost adjustment coefficient, taking 1.0.

[0189] Among them, the total factor weight W ij is calculated by formula (1), and the eigenvalue f of different factor levels ijk is calculated by formula (2).

[0190] Application of cost level difference

[0191] Using the cost level difference, two aspects of work can be completed according to its function. One is to sort and compare cost blocks or oilfields; the other is to allocate the operating costs of oilfields according to the cost level difference.

[0192] (1) Comparison based on the reference block or oilfield

[0193] Calculate the cost level difference of the block or oilfield using formula (3), select the reference comparison block or oilfield, and the reference cost value of the block or oilfield to be compared can be obtained from the following formula.

[0194]

[0195] In the formula: CB i —— Crude oil cost to be determined for the i-th block or oilfield;

[0196] CB0 —— Crude oil cost of the comparison block or oilfield;

[0197] —— Cost level difference coefficient of the i-th block or oilfield;

[0198] —— Cost level difference coefficient of the comparison block or oilfield.

[0199] At the same time, sort the cost blocks or oilfields according to the calculated cost level difference. The smaller the cost level difference, the smaller the corresponding unit operation.

[0200] Scope of application: suitable for cost comparison and ranking between blocks or oil fields.

[0201] Example 1:

[0202] Use the established cost differential model to conduct differential comparison and ranking on Oil Production Plants 1 to 6 in Oil Region A, and take Oil Production Plant 1 as the comparison benchmark to obtain the reference costs of other plants. The basic data parameters and the obtained cost differential coefficients of Oil Production Plants 1 to 6 are shown in Tables 22 and 23 respectively. Among them, Table 22 is the basic data value, and the characteristic values in Table 23 are obtained by looking up the corresponding factor characteristic values in Table 21 according to the data values in Table 22.

[0203] Table 22 Basic Data Table of Oil Production Plants 1 to 6 in Oil Region A

[0204]

[0205] Table 23 Characteristic Value Result Table of Oil Production Plants 1 to 6 in Oil Region A

[0206]

[0207]

[0208] Taking the development factor differential coefficient of Plant 1 as an example, it can be seen from Table 23 that the development factors include 4 parameters: liquid production per day, oil production per day, water injection per day, and well pattern density, and the characteristic values are 1.138, 1.285, 1.00, and 1.00 in sequence. Looking up Table 21, it can be known that the weights of the 4 parameters are 0.1026, 0.1196, 0.0411, and 0.0846. Using formula (3), the sum after multiplying in sequence is 0.394. Similarly, the cost differential coefficients of each factor of the other plants can be obtained by summing the products of the weights of their corresponding parameters and their characteristic values. The total cost differential coefficient of each plant is the sum of the differential coefficients of each factor. Calculate the cost differential coefficients of each plant and the oil field ranking results, as shown in Table 24.

[0209] Table 24 Comparison Table of Differential Coefficients of Each Plant in Oil Region A

[0210]

[0211] Using formula (4), taking the cost of Oil Production Plant 1 as the differential comparison benchmark. If the differential cost for comparison is 204.83 yuan / t, then the reference costs of other plants obtained are shown in Table 25.

[0212] Table 25 Reference Cost Result Table of Each Plant in Oil Region A

[0213]

[0214] (2) Distribution of the total oil field operation cost

[0215] Determine the unit operating cost of each combined unit respectively according to the total operating cost of the whole oilfield and the production of waterflooding wells in each combined unit, and in combination with the determined cost differential.

[0216] Let the total operating cost of the oilfield be CB, the total production be Q, and the unit cost, oil production, and cost differential of the i-th combined unit be CB i , Q i , And let the k-th combined unit be the comparison benchmark, k ∈ {i}, i = 1, 2, …, n (n is the number of combined units), then the benchmark cost, oil production, and cost differential of the oilfield are CB k , Q k ,

[0217] Using formula (2), the calculation formula for the total operating cost of the oilfield can be obtained as follows:

[0218]

[0219] Among them:

[0220] The unit operating cost CB k of the benchmark unit can be obtained from the above formula, and the formula is as follows:

[0221]

[0222] The unit operating cost value of each combined unit can be obtained using formula (5).

[0223] The cost differential in formulas (4) and (5) uses the normalized differential value.

[0224] Scope of application: Suitable for allocating the total operating cost when the total operating cost and the production composition of the block (oilfield) are known.

[0225] Example 2:

[0226] The operating cost of Oil Region A in 2001 was 223.21 yuan / ton, and the total operating cost was 7957.485 million yuan. First, normalize the determined cost differential to ensure that the total cost remains unchanged after allocation. The allocated costs of different units can be obtained using formulas (4) and (5). The production composition and cost allocation results of Oil Production Plants 1 to 6 are shown in Table 26.

[0227] Table 26 Allocated Cost Results of Each Plant in Oil Region A

[0228]

Claims

1. An analytical method for determining the operating cost differential of a developed waterflooding oilfield, characterized in that Including: S1. Determine the factors at all levels that affect the operation cost; S2. Determine the weight values and total weight value of the factors at all levels; S3. Determine the characteristic values of the factors at all levels; S4. Determine the cost differential calculation model: Where: F CB - Total cost level difference; W ij - Total weight of factors; f ijk - Eigenvalues of different levels of factors; N - The number of primary factors; M(i) - The number of secondary factors of the i-th primary factor; P(j) - The number of levels of the j-th secondary factor; β - Cost adjustment coefficient, taking 1.

0.

2. The analysis method for determining the operating cost differential of a developed waterflooding oilfield according to claim 1, characterized in that: The total weight value W of the factors in step S4 ij is as follows: W ij = W i ·W j Where: W i - Weight of the first-level factor, W j - Weight of the second-level factor in the first-level factor.

3. The analysis method for determining the operating cost differential of a developed waterflooding oilfield according to claim 1, characterized in that: The eigenvalue f of different levels of the factors in step S4 ijk is as follows: f ijk = (f ik + f jk ) / 2 where: f ik - Eigenvalues of different levels of factors determined by the statistical method; f jk - Eigenvalues of different levels of factors determined by the regression method.

4. The analysis method for determining the operation cost differential of a developed waterflooding oilfield according to claim 3, characterized in that: The statistical method is as follows: Classify a certain type of data, calculate the arithmetic mean of the classification results, determine the initial reference characteristic value, and compare the remaining values with the initial reference characteristic value to obtain the corresponding characteristic values.

5. The analysis method for determining the operating cost differential of a developed waterflooding oilfield according to claim 4, characterized in that: The regression method is as follows: Statistically regress a certain type of data, establish a mathematical calculation formula, calculate the average value after classification according to the regression formula, determine the initial reference characteristic value, and compare the remaining values with the initial reference characteristic value to obtain the corresponding characteristic values.

6. The analysis method for determining the operating cost differential of a developed waterflooding oilfield according to claim 1, characterized in that: The primary factors that affect the operation cost include geological factors, development factors, and natural condition factors; among them, the secondary factors in geological factors include reservoir burial depth, reserve abundance, and permeability, the secondary factors in development factors include well pattern density, oil production per well per day, liquid production per well per day, and water injection per well per day, and the secondary factors in natural condition factors include distance and landform.

7. The analysis method for determining the operating cost differential of a developed waterflooding oilfield according to claim 6, characterized in that: The weight W of the first-level factors in the step S2 i is: the weight of the geological factor is 0.5402, the weight of the development factor is 0.3478, and the weight of the natural condition is 0.

112.

8. The analysis method for determining the operation cost differential of a developed waterflooding oilfield according to claim 7, characterized in that: The weight W of the secondary factors in the step S2 j is as follows: among the secondary factors of geological factors, the weight of reserve abundance is 0.4846, the weight of reservoir burial depth is 0.3899, and the weight of permeability is 0.1255; Among the secondary factors in development factors, the weight value of well pattern density is 0.2432, the weight value of oil production per well per day is 0.2949, the weight value of liquid production per well per day is 0.3438, and the weight value of water injection per well per day is 0.1181; among the secondary factors in natural conditions, the weight value of distance is 0.25, and the weight value of landform is 0.

75.

9. An application of the operation cost differential of a developed waterflooded oilfield according to any one of claims 1-8, characterized in that: Calculate the block cost differential using the cost differential calculation model, select the reference comparison block, and calculate the reference cost value of the block to be compared according to the following formula: Where: CB i —— the crude oil cost to be determined for the i-th block or oilfield; CB0 - The crude oil cost of the comparison block or oilfield; ——Cost differential coefficient of the i-th block or oilfield; ——Cost differential coefficient for comparing blocks or oilfields.

10. The application of the differential operation cost of the developed waterflooded oilfield according to claim 9, characterized in that: Allocate the unit operation cost of each combined unit according to the total operation cost of the entire oilfield and the water - drive well production composition of each combined unit, combined with the determined cost differential; (1) The total operation cost of the oilfield is: In the formula: CB - The total operation cost of the oilfield; CB k —— Benchmark cost of the oilfield; ——Cost differential; Q i —— Oil production of the i-th combined unit; ——Cost difference of the i-th combined unit; n - The number of combined units; (2) Benchmark unit operation cost CB k is as follows: (3) Calculate the unit operation cost of each combined unit according to formula (4).

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