Shale oil dessert identification method based on mineral components

Abstract

The invention relates to the technical field of continental shale oil exploration resource evaluation dessert identification, in particular to a shale oil dessert identification method based on mineral components. The method comprises the following steps: dividing lithofacies by utilizing three-end-member components, and establishing a total organic carbon TOC curve, a chloroform asphalt 'A' curve, an effective porosity Por curve and a fracture density H curve of different lithofacies; determining U oiliness curves of different lithofacies by using the total organic carbon TOC and the chloroform bitumen 'A', determining U mobility curves of different lithofacies by using the S1 and the total organic carbon TOC, and determining U reservoir curves of different lithofacies by using the effective porosity Por and the fracture density H. And establishing different lithofacies dessert index formulas by using U mobility, U oiliness and U reservoir. And determining lower limits of different lithofacies dessert indexes, and judging a dessert development section. Different lithofacies dessert models are considered, and the identification parameters comprise oiliness, storage and mobility, so that the method is more comprehensive and systematic, and the mixed shale oil dessert can be accurately identified.

Description

Technical Field

[0001] The present invention relates to the technical field of sweet spot identification in continental shale oil exploration resource evaluation, and in particular to a shale oil sweet spot identification method based on mineral components. Background Art

[0002] The Fengcheng Formation mixed shale oil is controlled by the fan delta front subfacies and lakeside subfacies sedimentary systems, including two major sources of terrigenous debris and endogenous chemistry. The rock structure is complex, the high-cycle characteristics are obvious, the centimeter-level laminae are developed, and the rock naming is difficult. The high-quality source rocks of alkaline lakes are quite different from conventional source rocks. Their hydrocarbon-generating parent materials are mainly bacteria, algae and amorphous bodies, while conventional source rocks are mostly plankton, which greatly increases their hydrocarbon production rate, which is twice that of traditional lake-phase high-quality source rocks. The evaluation parameters and indicators of their source rocks are also different from those of conventional source rocks, making the evaluation of source rocks difficult. In addition, due to the influence of the sedimentary environment, the mixed shale oil sweet spots are thin interlayers, not concentrated, and difficult to identify. In order to solve the above problems, a shale oil sweet spot identification method based on mineral components was invented. Summary of the invention

[0003] In view of the above problems, the present invention provides a shale oil sweet spot identification method based on mineral components, which is used to improve the accuracy of sweet spot identification results for mixed rock type shale oil.

[0004] A method for identifying shale oil sweet spots based on mineral components, the method comprising:

[0005] Obtain mineral analysis data of wells in the target block and divide the lithofacies;

[0006] The total organic carbon T 0C curve, chloroform bitumen "A" curve, effective porosity Por curve and fracture density H curve of different lithofacies were established using well data;

[0007] According to the total organic carbon TOC curve, chloroform bitumen "A" curve, effective porosity Por curve and fracture density H curve, the U of different lithofacies can be determined. 可动性 , U 含油性 and U 储集性 curve;

[0008] According to U 可动性 , U 含油性 and U 储集性 The curves are used to calculate the sweet spot index of different lithofacies;

[0009] The lower limits of sweet spot indexes of different lithofacies are determined based on well test data to judge the sweet spot development section.

[0010] Further, the lithofacies are divided into:

[0011] The lithofacies are divided into felsic lithofacies, mixed lithofacies and dolomitic lithofacies using the three-terminal component plate.

[0012] Furthermore, the total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve and fracture density H curve of different lithofacies are established, including:

[0013] The calibration is based on the experimental analysis results of core samples in the target block, and the total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve and fracture density H curve are continuously calculated using logging data.

[0014] Furthermore, the different lithofacies U 可动性 , U 含油性 and U 储集性 Curves, including:

[0015] Use U 可动性 =S1 / T0C determines different facies U 可动性 curve;

[0016] In the formula, rock pyrolysis S1 and total organic carbon TOC are obtained through experiments, and S1 and TOC are fitted to obtain the U mobility curve.

[0017] Furthermore, the different lithofacies U 可动性 , U 含油性 and U 储集性 Curves, including:

[0018] Use U 含油性 =dU Toc +eU ‘A’ Determine the different lithofacies U 含油性 curve;

[0019] In the formula, U Toc is the total organic carbon T0C curve, U ‘A’ It is the chloroform asphalt "A" curve;

[0020] d and e are weight coefficients, which are determined by the grey relational analysis statistical method.

[0021] Furthermore, the different lithofacies U 可动性 , U 含油性 and U 储集性 Curves, including:

[0022] Use U 储集性 =fU por +gU H Determine the different lithofacies U 储集性 curve;

[0023] In the formula, U por is the effective porosity Por curve, U His the crack density H curve;

[0024] f and g are weight coefficients, which are determined by the grey relational analysis statistical method.

[0025] Furthermore, according to U 可动性 , U 含油性 and U 储集性 The curve calculates the sweet spot index of different facies, including:

[0026] f 长英质岩相甜点指数 =a1U 可动性 +b1U 含油性 +c1U 储集性 ;

[0027] f 长英质岩相甜点指数 =a2U 可动性 +b2U 含油性 +c2U 储集性 ;

[0028] f 长英质岩相甜点指数 =a3U 可动性 +b3U 含油性 +c3U 储集性 ;

[0029] In the formula, a1, a2, a3, b1, b2, b3, c1, c2 and c3 are weight coefficients, which are determined by the grey correlation analysis statistical method.

[0030] Furthermore, the lower limits of sweet spot indexes of different lithofacies are determined based on well test data, including:

[0031] The oil test result data and the calculated f 长英质岩相甜点数 、f 长英质岩相甜点指数 、f 长英质岩相甜点指数 , the lower limit of the oil layer is determined to be the lower limit of the lithofacies sweet spot index.

[0032] The present invention has at least the following beneficial effects:

[0033] The present invention establishes for the first time a geological sweet spot evaluation system with lithofacies as the core, which can accurately identify vertical mixed shale oil sweet spots. The identification parameters include three aspects: oil content, reservoir and mobility. It is more comprehensive and systematic, and can accurately identify mixed shale oil sweet spots.

[0034] Other features and advantages of the present invention will be described in the following description, and partly become obvious from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 The figure is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Sweet spot identification is an important part of shale oil exploration. The success rate of sweet spot identification is the basis of drilling success rate. The technology of the present invention can provide accurate sweet spot identification results for mixed rock shale oil. Since alkaline lake shale oil is affected by the sedimentary environment, its source rock evaluation and reservoir performance evaluation are different from conventional reservoirs. The sweet spot identification method is also different from other sweet spot identifications, which is difficult.

[0039] The present invention provides a method for accurately identifying mixed shale oil sweet spots. The method includes: using mineral analysis data to divide lithofacies using three-terminal components, and using known well data to establish total organic carbon TOC curves, chloroform asphalt "A" curves, effective porosity Por curves, and fracture density H curves for different lithofacies. Using total organic carbon TOC and chloroform asphalt "A" to determine different lithofacies U 含油性 Curve, using S1 and total organic carbon TOC to determine different lithofacies U 可动性 Curve, using effective porosity Por and fracture density H to determine different lithofacies U 储集性 Curve. Use U 可动性 , U 含油性 , U 储集性 Establish the index formula of sweet spots in different lithofacies. Based on the formula, determine the lower limit of sweet spot index in different lithofacies and judge the sweet spot development section. This method takes into account the different lithofacies sweet spot models. The identification parameters include three aspects: oil content, reservoir and mobility. It is more comprehensive and systematic, and can accurately identify mixed accumulation shale oil sweet spots.

[0040] like Figure 1 As shown, the present invention provides a method for identifying shale oil sweet spots based on mineral components, the method comprising:

[0041] S1, obtain mineral analysis data of wells in the target block and divide the lithofacies;

[0042] S2, using well data to establish total organic carbon TOC curves, chloroform bitumen "A" curves, effective porosity Por curves and fracture density H curves for different lithofacies;

[0043] S3, according to the total organic carbon TOC curve, chloroform bitumen "A" curve, effective porosity Por curve and fracture density H curve, determine the different lithofacies U 可动性 , U 含油性 and U 储集性 curve;

[0044] S4, according to U 可动性 , U 含油性 and U 储集性 The curves are used to calculate the sweet spot index of different lithofacies;

[0045] S5, determine the lower limit of sweet spot index of different lithofacies based on well test data and judge the sweet spot development section.

[0046] In specific implementation, the characteristic curves of different lithofacies are fitted using well data to obtain calculation expressions of the characteristic curves, and the results of the calculation expressions are used as variables to further calculate the sweet spot indexes of different lithofacies.

[0047] In one embodiment, dividing the lithofacies includes:

[0048] The lithofacies are divided into felsic lithofacies, mixed lithofacies and dolomitic lithofacies using the three-terminal component plate.

[0049] In one embodiment, a total organic carbon TOC curve, a chloroform bitumen "A" curve, an effective porosity Por curve and a fracture density H curve of different lithofacies are established, including:

[0050] The calibration is based on the experimental analysis results of core samples in the target block, and the total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve and fracture density H curve are continuously calculated using logging data.

[0051] In one embodiment, different facies U are determined 可动性 , U 含油性 and U 储集性 Curves, including:

[0052] Use U 可动性 =S1 / T0C determines different lithofacies U 可动性 curve;

[0053] In the formula, rock pyrolysis S1 and total organic carbon TOC are obtained through experiments, and S1 and TOC are fitted to obtain the U mobility curve.

[0054] In one embodiment, different facies U are determined 可动性 , U 含油性 and U 储集性 Curves, including:

[0055] Use U 含油性 =dU Toc +eU ‘A’ Determine the different lithofacies U 含油性 curve;

[0056] In the formula, U Toc is the total organic carbon T0C curve, U ‘A’ It is the chloroform asphalt "A" curve;

[0057] d and e are weight coefficients, which are determined by the grey relational analysis statistical method.

[0058] In one embodiment, different facies U are determined 可动性 , U 含油性 and U 储集性 Curves, including:

[0059] Use U 储集性 =fU por +gU H Determine the different lithofacies U 储集性 curve;

[0060] In the formula, U por is the effective porosity Por curve, U H is the crack density H curve;

[0061] f and g are weight coefficients, which are determined by the grey relational analysis statistical method.

[0062] In one embodiment, according to U 可动性 , U 含油性 and U 储集性 The curve calculates the sweet spot index of different facies, including:

[0063] f 长英质岩相甜点指数 =a1U 可动性 +b1U 含油性 +c1U 储集性 ;

[0064] f 长英质岩相甜点指数 =a2U 可动性 +b2U 含油性 +c2U 储集性 ;

[0065] f 长英质岩相甜点指数 =a3U 可动性 +b3U 含油性 +c3U 储集性 ;

[0066] In the formula, a1, a2, a3, b1, b2, b3, c1, c2 and c3 are weight coefficients, which are determined by the grey correlation analysis statistical method.

[0067] In one embodiment, determining the lower limits of sweet spot indexes of different lithofacies based on well test data includes:

[0068] The oil test results and the calculated f 长英质岩相甜点指数 、f 长英质岩相甜点指数 、f 长英质岩相甜点指数 , the lower limit of the oil layer is determined to be the lower limit of the lithofacies sweet spot index.

[0069] In order to enable those skilled in the art to better understand the present invention, the principle of the present invention is described as follows in conjunction with the accompanying drawings:

[0070] The present invention is the first to consider the different characteristics of different lithofacies sweet spots, comprehensively consider the oil content, storage capacity and mobility of shale oil reservoirs, and establish a different lithofacies sweet spot identification model, which is more comprehensive and systematic and can accurately identify mixed shale oil sweet spots.

[0071] "Sweet spots" refer to the development sections of high-quality shale oil reservoirs under the background of mature source rock development, which can be transformed into sections with higher development benefits.

[0072] The embodiment of the present invention provides a method for identifying sweet spots in mixed shale oil reservoirs. Figure 1 As shown, the steps include:

[0073] 1. Obtain mineral analysis data of wells in the target block and use the three-terminal component chart to divide the lithofacies. It can be divided into felsic lithofacies, mixed lithofacies and dolomitic lithofacies.

[0074] 2. Establish the total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve, and fracture density H curve of different lithofacies with known well data. The above-mentioned total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve, and fracture density H curve are calibrated according to the experimental analysis results of core samples in the target block and are continuously calculated using logging data.

[0075] 3. Determine the different lithofacies U 可动性 , U 含油性 , U 储集性 Curve. Use U 可动性 =S1 / TOC determines different lithofacies U 可动性 Curve, use U 含油性 =dU Toc +eU ‘A’ Determine the different lithofacies U 含油性 Curve, use U 储集性 =fU por +gU H Determine the different lithofacies U储集性 curve.

[0076] 4. Calculate the sweet spot index of different lithofacies. The weight coefficients of a, b, c, d, e, f and g in the formula are determined by the grey correlation analysis statistical method.

[0077] f 长英质岩相甜点指数 =a1U 可动性 +b1U 含油性 +c1U 储集性

[0078] f 混合岩相甜点指数 =a2U 可动性 +b2U 含油性 +c2U 储集性

[0079] f 云质岩相甜点指数 =a3U 可动性 +b3U 含油性 +c3U 储集性

[0080] The sweet spot index is calculated based on the formula, and the lower limit of the sweet spot index of different lithofacies is determined according to the known well test data to judge the sweet spot development section.

[0081] The sweet spot index constructed in the present invention is based on different lithofacies characteristics for the first time, and different lithofacies index calculation models are established. The identification index established by the sub-model is more accurate. At the same time, the index calculation can comprehensively reflect the quality of shale oil source rocks, reservoir storage capacity and oil content. The final classification index calculation standard is calibrated with actual oil test data, and the identification result is highly reliable.

[0082] The steps of using grey relational analysis are:

[0083] 1. Determine the analysis indicator system according to the analysis purpose and collect analysis data.

[0084] Suppose n data sequences form the following matrix:

[0085]

[0086] Where m is the number of indicators, X′ i =(x′ i (1), x′ i (2), …, x′ i (m)) T , i=1,2,…,n;

[0087] 2. Determine the reference data column

[0088] The reference data column should be an ideal comparison standard. The reference data column can be composed of the optimal value (or the worst value) of each indicator, or other reference values ​​can be selected according to the evaluation purpose. It is denoted as X′0=(x′0(1), x′0(2), …, x′0(m));

[0089] 3. Non-dimensionalize indicator data

[0090] Since the physical meanings of various factors in the system are different, the dimensions of the data may not be the same, which makes it difficult to compare or to get the correct conclusion when comparing. Therefore, dimensionless data processing is generally required when performing grey relational analysis.

[0091] Commonly used dimensionless methods include averaging, initialization, and Transformation, etc.

[0092]

[0093]

[0094] The dimensionless data sequence forms the following matrix:

[0095]

[0096] 4. Calculate the absolute difference between the corresponding elements of each evaluated object indicator sequence (comparison sequence) and the reference sequence one by one

[0097] That is |x0(k)-x i (k)|(k=1,…,m,i=1,…,n,n is the number of evaluated objects),

[0098] 5. OK and

[0099] 6. Calculate the correlation coefficient

[0100] The correlation coefficient of each comparison sequence and the corresponding element of the reference sequence is calculated by the following formula:

[0101]

[0102] Where ρ is the resolution coefficient, 0<ρ<1. The smaller ρ is, the greater the difference between the correlation coefficients, and the stronger the discrimination ability is. Usually ρ is 0.5;

[0103] When the optimal value (or the worst value) of each indicator is used to form a reference data column to calculate the correlation coefficient, an improved and simpler calculation method can also be used:

[0104]

[0105] The improved method can not only omit the third step, making the calculation simpler, but also avoid some negative effects of dimensionless on the index function;

[0106] 7. Calculate the association order

[0107] For each evaluation object (comparison sequence), the mean of the correlation coefficient between its individual indicators and the corresponding elements of the reference sequence is calculated to reflect the correlation relationship between each evaluation object and the reference sequence, which is called the correlation sequence and recorded as:

[0108]

[0109] 8. If the various indicators play different roles in the comprehensive evaluation, the weighted average of the correlation coefficient can be calculated.

[0110]

[0111] Where W k is the weight of each indicator.

[0112] 9. Based on the correlation sequence of each observed object, the analysis results are obtained.

[0113] The present invention has been applied to wells such as Maye 1, and four sets of sweet spots have been identified in the vertical development of Fengcheng Formation in Mahu Sag. The results of the identification of Maye 1 well have a good correspondence with the production profile of oil testing, and high-yield industrial oil flow has been obtained. The horizontal wells deployed for these four sets of sweet spots have achieved good exploration results, among which Maye 1H drilling sweet spot No. 1 has a high production of 100 cubic meters of oil per day, which fully proves the reliability of the method.

[0114] For the first time, a geological sweet spot evaluation system with lithofacies as the core was established, which can accurately identify vertical mixed shale oil sweet spots. The identification parameters include three aspects: oil content, reservoir and mobility. It is more comprehensive and systematic, and can accurately identify mixed shale oil sweet spots. The present invention is an essential technology for shale oil exploration and development. From the perspective of economic benefits, the present invention will provide technical support for the effective use of mixed rock oil reserves and the increase of reserves.

[0115] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying shale oil sweet spots based on mineral components, characterized in that: The method comprises: Obtain mineral analysis data of wells in the target block and divide the lithofacies; Using well data, we established TOC curves, chloroform bitumen "A" curves, effective porosity Por curves and fracture density H curves for different lithofacies. According to the total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve and fracture density H curve, the U of different lithofacies can be determined. 可动性 , U 含油性 and U 储集性 curve; According to U 可动性 , U 含油性 and U 储集性 Curve calculation of sweet spot index of different lithofacies; The lower limits of sweet spot indexes of different lithofacies are determined based on well test data to judge the sweet spot development section.

2. The method for identifying shale oil sweet spots based on mineral components according to claim 1, characterized in that: Divide the lithofacies, including: The lithofacies are divided into felsic lithofacies, mixed lithofacies and dolomitic lithofacies using the three-terminal component plate.

3. The method for identifying shale oil sweet spots based on mineral components according to claim 1, characterized in that: Establish the total organic carbon TOC curve, chloroform bitumen "A" curve, effective porosity Por curve and fracture density H curve of different lithofacies, including: The calibration is based on the experimental analysis results of core samples in the target block, and the total organic carbon TOC curve, chloroform asphalt "A" curve, effective porosity Por curve and fracture density H curve are continuously calculated using logging data.

4. The method for identifying shale oil sweet spots based on mineral components according to claim 1, characterized in that: Determine the different lithofacies U 可动性 , U 含油性 and U 储集性 Curves, including: Use U 可动性 =S1 / TOC determines different lithofacies U 可动性 curve; In the formula, rock pyrolysis S1 and total organic carbon TOC are obtained through experiments, and S1 and TOC are fitted to obtain the U mobility curve.

5. The method for identifying shale oil sweet spots based on mineral components according to claim 1, characterized in that: Determine the different lithofacies U 可动性 , U 含油性 and U 储集性 curve, include: Use U 含油性 =dU Toc +eU ‘A’ Determine the different lithofacies U 含油性 curve; In the formula, U Toc is the total organic carbon TOC curve, U ‘A’ is the chloroform asphalt "A" curve; d and e are weight coefficients, which are determined by the grey relational analysis statistical method.

6. The method for identifying shale oil sweet spots based on mineral components according to claim 1, characterized in that: Determine the different lithofacies U 可动性 , U 含油性 and U 储集性 Curves, including: Use U 储集性 =fU por +gU H Determine the different lithofacies U 储集性 curve; In the formula, U por is the effective porosity Por curve, U H is the crack density H curve; f and g are weight coefficients, which are determined by the grey relational analysis statistical method.

7. The method for identifying shale oil sweet spots based on mineral components according to claim 1, characterized in that: According to U 可动性 , U 含油性 and U 储集性 The curve calculates the sweet spot index of different facies, including: in 长英质岩相甜点指数 =a1U 可动性 +b1U 含油性 +c1U 储集性 ; f 长英质岩相甜点指数 =a2U 可动性 +b2U 含油性 +c2U 储集性 ; <h2 style=";text-align:left;direction:ltr">f<h2 style=";text-align:left;direction:ltr"> 长英质岩相甜点指数 <h2 style=";text-align:left;direction:ltr"> =a3U<h2 style=";text-align:left;direction:ltr"> 可动性 <h2 style=";text-align:left;direction:ltr"> +b3U<h2 style=";text-align:left;direction:ltr"> 含油性 <h2 style=";text-align:left;direction:ltr"> +c3U<h2 style=";text-align:left;direction:ltr"> 储集性 <h2 style=";text-align:left;direction:ltr"> ; In the formula, a1, a2, a3, b1, b2, b3, c1, c2 and c3 are weight coefficients, which are determined by the grey correlation analysis statistical method.

8. The method for identifying shale oil sweet spots based on mineral components according to claim 7, characterized in that: The lower limits of sweet spot indexes of different lithofacies are determined based on well test data, including: The oil test result data and the calculated f 长英质岩相甜点指数 、f 长英质岩相甜点指数 、f 长英质岩相甜点指数 , the lower limit of the oil layer is determined to be the lower limit of the lithofacies sweet spot index.

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