Crack identification method and device based on rock mineral components and conventional logging

Abstract

The invention relates to the technical field of oil and gas exploration and development, in particular to a crack identification method and device based on rock mineral components and conventional logging, and the method comprises the steps: obtaining rock mineral component content data and logging data of a target reservoir in a research area, the logging data comprises a gamma curve, a natural potential curve, a sound wave time difference curve, a neutron curve, deep lateral resistivity, shallow lateral resistivity and a well diameter curve; determining the brittleness index of the target reservoir according to the rock mineral components; and determining a fracture strength curve of the target reservoir based on a fracture strength curve model according to the brittleness index and the logging data of the target reservoir. According to the method, multi-parameter response characteristics such as mineral components, fracture toughness and electrical properties of a fracture development layer section are fully considered, and a fracture strength curve is constructed; the accuracy of the crack strength curve is improved, and then the accuracy of crack identification is improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and is a method and device for fracture identification based on rock mineral composition and conventional logging. Background Technology

[0002] Fractures, serving as effective reservoir spaces and primary seepage channels in tight reservoirs, significantly impact their high and stable production. Tight reservoirs, having generally undergone deep burial, intense compaction, and complex diagenesis during geological evolution, have experienced varying degrees of compaction, exhibiting greater heterogeneity and anisotropy compared to conventional reservoirs. These unique characteristics result in complex fracture development patterns and strong heterogeneity in tight reservoirs; therefore, accurate fracture identification is crucial for reservoir evaluation and oil and gas development.

[0003] Patent CN118169746A discloses a fracture identification method based on acoustic transit time and density logging. The method includes two steps: Step 1: Selecting a standard well and determining the standard rock formation section; Step 2: Obtaining the calculated acoustic transit time porosity Φ of the standard rock formation section. ac The parameters used; Step 3: Calculate the acoustic transit time porosity Φ ac Using the parameters, calculate the fracture development index *r* for other strata segments besides the standard stratum segment; Step four: If the fracture development index *r* for other strata segments > 1, then it is confirmed that other strata segments have developed fractures; if the fracture development index *r* for other strata segments < 1, then it is confirmed that other strata segments do not have developed fractures. However, single porosity logging series are often affected by reservoir lithology, causing errors in interpretation results. In addition, special logging methods for identifying fractures are expensive per well, and seismic data methods for predicting fractures have low resolution and are difficult to identify small fractures.

[0004] Therefore, it is urgent to establish a multi-parameter comprehensive fracture evaluation model to achieve reservoir fracture identification throughout the entire well section. Summary of the Invention

[0005] This invention provides a fracture identification method based on rock mineral composition and conventional well logging, which overcomes the shortcomings of the prior art and can effectively solve the problems of high cost per well and low resolution of fracture identification methods based on seismic data.

[0006] One of the technical solutions of this invention is achieved through the following measures: a fracture identification method based on rock mineral composition and conventional well logging, comprising:

[0007] Obtain rock and mineral composition data and well logging data of the target reservoir in the study area. The well logging data includes gamma curves, spontaneous potential curves, sonic transit time curves, neutron curves, deep lateral resistivity, shallow lateral resistivity (RI), and well caliper curve (CAL).

[0008] The brittleness index of the target reservoir is determined based on the rock mineral composition;

[0009] Based on the brittleness index and well logging data of the target reservoir, the fracture strength curve of the target reservoir is determined using a fracture strength curve model.

[0010] The expression for the crack strength curve model is as follows:

[0011] F=a×GR-b×SP+c×RT-d×RI+e×CNL+f×AC+g×I B -h×CAL-z Formula 1

[0012] In the formula, F is the fracture intensity curve, GR is the logging gamma value of the target reservoir, SP is the logging spontaneous potential value of the target reservoir, RT is the logging deep lateral resistivity value of the target reservoir, RI is the logging shallow lateral resistivity value of the target reservoir, CNL is the logging neutron value of the target reservoir, AC is the logging sonic transit time value of the target reservoir, and I... B denoted as the brittleness index of the target reservoir, CAL as the logging diameter value of the target reservoir, and a, b, c, d, e, f, g, h, and z as coefficients.

[0013] The following are further optimizations and / or improvements to one of the above-mentioned technical solutions:

[0014] The target reservoir is a tight formation.

[0015] The mineral composition of the aforementioned rocks includes the contents of quartz, feldspar, calcite, illite, montmorillonite, and chlorite.

[0016] The above-mentioned determination of the brittleness index of the target reservoir based on rock mineral composition includes:

[0017] The fracture toughness data of the target reservoir are obtained based on the rock mineral composition content of the target reservoir.

[0018] The brittleness index of the target reservoir is determined based on the fracture toughness data in the target reservoir.

[0019] The above-mentioned fracture toughness data obtained from the rock mineral composition of the target reservoir includes: determining the overall fracture toughness of the target reservoir according to the following formula:

[0020]

[0021] In the formula, K c Let Kci be the overall fracture toughness of the target reservoir, and W be the fracture toughness of the i-th mineral. i Let represent the content of the i-th mineral.

[0022] The above-mentioned determination of the brittleness index of the target reservoir based on fracture toughness data includes determining the brittleness index of the target reservoir according to the following formula:

[0023]

[0024] In the formula, I B K is the brittleness index of the target reservoir. Cmax K represents the maximum value of the comprehensive fracture toughness at various depths in the target reservoir. Cmin K represents the minimum comprehensive fracture toughness at various depths within the target reservoir. c The comprehensive fracture toughness of the target reservoir.

[0025] The above also includes evaluating the degree of crack development based on the crack strength curve value, including: the degree of crack development is positively correlated with the value of the crack strength curve.

[0026] The second technical solution of the present invention is achieved through the following measures: a fracture identification device based on rock mineral composition and conventional well logging, comprising:

[0027] The data acquisition module obtains rock and mineral composition data and well logging data of the target reservoir within the study area. The well logging data includes gamma curves, spontaneous potential curves, sonic transit time curves, neutron curves, deep lateral resistivity, shallow lateral resistivity, and well diameter curves.

[0028] The brittleness index module determines the brittleness index of the target reservoir based on the rock mineral composition;

[0029] The fracture strength module determines the fracture strength curve of the target reservoir based on the brittleness index and well logging data, using a fracture strength curve model.

[0030] The expression for the crack strength curve model is as follows:

[0031] F=a×GR-b×SP+c×RT-d×RI+e×CNL+f×AC+g×I B -h×CAL-z

[0032] Formula 1

[0033] In the formula, F is the fracture intensity curve, GR is the logging gamma value of the target reservoir, SP is the logging spontaneous potential value of the target reservoir, RT is the logging deep lateral resistivity value of the target reservoir, RI is the logging shallow lateral resistivity value of the target reservoir, CNL is the logging neutron value of the target reservoir, AC is the logging sonic transit time value of the target reservoir, and I... B denoted as the brittleness index of the target reservoir, CAL as the logging diameter value of the target reservoir, and a, b, c, d, e, f, g, h, and z as coefficients.

[0034] The following are further optimizations and / or improvements to the second technical solution of the above invention:

[0035] The above-mentioned device also includes:

[0036] The evaluation module assesses the degree of crack development based on the crack strength curve values, including the positive correlation between crack development degree and crack strength curve values.

[0037] The fracture identification method based on rock mineral composition and conventional logging of the present invention fully considers the multi-parameter response characteristics of fracture development intervals, such as mineral composition, fracture toughness, and electrical properties, and constructs fracture strength curves; it improves the accuracy of fracture strength curves, thereby improving the accuracy of fracture identification. Attached Figure Description

[0038] Appendix Figure 1 This is a schematic diagram of the fracture identification device based on rock mineral composition and conventional well logging of the present invention.

[0039] Appendix Figure 2 This is a diagram showing the intensity analysis of fracture development in a single well in Embodiment 10 of the present invention. Detailed Implementation

[0040] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0041] The present invention will be further described below with reference to embodiments:

[0042] Example 1: This fracture identification method based on rock mineral composition and conventional logging includes:

[0043] Obtain rock and mineral composition data and well logging data of the target reservoir in the study area. The well logging data includes gamma curves, spontaneous potential curves, sonic transit time curves, neutron curves, deep lateral resistivity, shallow lateral resistivity, and well diameter curves.

[0044] The brittleness index of the target reservoir is determined based on the rock mineral composition;

[0045] Based on the brittleness index and well logging data of the target reservoir, the fracture strength curve of the target reservoir is determined using a fracture strength curve model.

[0046] The expression for the crack strength curve model is as follows:

[0047] F=a×GR-b×SP+c×RT-d×RI+e×CNL+f×AC+g×I B -h×CAL-z Formula 1

[0048] In the formula, F is the fracture intensity curve, GR is the logging gamma value of the target reservoir, SP is the logging spontaneous potential value of the target reservoir, RT is the logging deep lateral resistivity value of the target reservoir, RI is the logging shallow lateral resistivity value of the target reservoir, CNL is the logging neutron value of the target reservoir, AC is the logging sonic transit time value of the target reservoir, and I... B denoted as the brittleness index of the target reservoir, CAL as the logging diameter value of the target reservoir, and a, b, c, d, e, f, g, h, and z as coefficients.

[0049] Example 2: As an optimization of the above example, the target reservoir is a tight formation.

[0050] Example 3: As an optimization of the above examples, the rock mineral composition includes the contents of quartz, feldspar, calcite, illite, montmorillonite and chlorite.

[0051] Example 4: As an optimization of the above examples, determining the brittleness index of the target reservoir based on rock mineral composition includes:

[0052] The fracture toughness data of the target reservoir are obtained based on the rock mineral composition content of the target reservoir.

[0053] The brittleness index of the target reservoir is determined based on the fracture toughness data in the target reservoir.

[0054] Example 5: As an optimization of the above examples, obtaining the fracture toughness data of the target reservoir based on the rock mineral composition content of the target reservoir includes: determining the comprehensive fracture toughness of the target reservoir according to the following formula:

[0055]

[0056] In the formula, K c Let Kci be the overall fracture toughness of the target reservoir, and W be the fracture toughness of the i-th mineral. i Let represent the content of the i-th mineral.

[0057] Example 6: As an optimization of the above examples, the brittleness index of the target reservoir is determined based on the fracture toughness data in the target reservoir, including determining the brittleness index of the target reservoir according to the following formula:

[0058]

[0059] In the formula, I B K is the brittleness index of the target reservoir. Cmax K represents the maximum value of the comprehensive fracture toughness at various depths in the target reservoir. Cmin K represents the minimum comprehensive fracture toughness at various depths within the target reservoir. c The comprehensive fracture toughness of the target reservoir.

[0060] Example 7: As an optimization of the above embodiment, it further includes evaluating the degree of crack development based on the crack strength curve value, including: the degree of crack development is positively correlated with the value of the crack strength curve. That is: the larger the value of the crack strength curve, the higher the degree of crack development; the smaller the value of the crack strength curve, the lower the degree of crack development.

[0061] Example 8: This fracture identification device based on rock mineral composition and conventional logging includes:

[0062] The data acquisition module obtains rock and mineral composition data and well logging data of the target reservoir in the study area. The rock and mineral composition data includes the contents of quartz, feldspar, calcite, illite, montmorillonite and chlorite. The well logging data includes gamma curve, spontaneous potential curve, sonic transit time curve, neutron curve, deep lateral resistivity, shallow lateral resistivity RI and well caliper curve CAL.

[0063] The brittleness index module determines the brittleness index of the target reservoir based on the rock mineral composition;

[0064] The fracture strength module determines the fracture strength curve of the target reservoir based on the brittleness index and well logging data, using a fracture strength curve model.

[0065] The expression for the crack strength curve model is as follows:

[0066] F=a×GR-b×SP+c×RT-d×RI+e×CNL+f×AC+g×I B -h×CAL-z

[0067] Formula 1

[0068] In the formula, F is the fracture intensity curve, GR is the logging gamma value of the target reservoir, SP is the logging spontaneous potential value of the target reservoir, RT is the logging deep lateral resistivity value of the target reservoir, RI is the logging shallow lateral resistivity value of the target reservoir, CNL is the logging neutron value of the target reservoir, AC is the logging sonic transit time value of the target reservoir, and I... B denoted as the brittleness index of the target reservoir, CAL as the logging diameter value of the target reservoir, and a, b, c, d, e, f, g, h, and z as coefficients.

[0069] Example 9: As an optimization of the above embodiments, it further includes:

[0070] The evaluation module assesses the degree of crack development based on the crack strength curve values, including the positive correlation between crack development degree and crack strength curve values.

[0071] Example 10: The specific implementation process of this fracture identification method based on rock mineral composition and conventional logging is as follows:

[0072] We obtained rock and mineral composition data, well logging data, and imaging logging data from wells already logged in the study area. Based on the response characteristics of fractures in imaging logging, we selected fracture-developed intervals as samples and compared and analyzed the rock and mineral composition data and conventional logging data corresponding to the sample intervals.

[0073] A calculation model for fracture toughness (Equation 2) was constructed, and the brittleness index was calculated according to Equation 3 based on the rock mineral composition data of the sample layer and the fracture toughness.

[0074] Based on the brittleness index and conventional logging response parameters, a fracture strength curve model for the study block was constructed. Regression fitting was used to determine the coefficients in the model. In this embodiment, the determined fracture strength curve model is: F = -0.024×GR - 0.052×SP + 0.001×RT - 0.001×RI + 0.051×CNL + 0.059×AC

[0075] +4.240×I B -0.217×CAL-5.034

[0076] In the formula, F is the fracture intensity curve, GR is the logging gamma value of the target reservoir, SP is the logging spontaneous potential value of the target reservoir, RT is the logging deep lateral resistivity value of the target reservoir, RI is the logging shallow lateral resistivity value of the target reservoir, CNL is the logging neutron value of the target reservoir, AC is the logging sonic transit time value of the target reservoir, and I... B CAL is the brittleness index of the target reservoir and the logging diameter value of the target reservoir.

[0077] Based on the fitted model, fracture strength was calculated in the reservoir from 1010 to 1280 m in the well to be evaluated, and the fracture strength curves from 1000 m to 1300 m were obtained. The results are shown in [Figure number missing]. Figure 2 Comparing the fracture intensity curves obtained from rock and mineral logging data with the actual imaging logging data, the actual imaging logging data identified 46 fractures in the 1010-1280m interval. Among them, 19 fractures were identified in the 1100-1125m interval, with the fracture intensity curve value increasing from 0 to 2.448, and an average value of 0.856. The fracture intensity curves responded to 17 fractures, with a matching rate of 89.47%. In the 1220-1260m interval, 25 fractures were identified, with the fracture intensity curve value increasing from 0 to 3.128, and an average value of 2.051. The fracture intensity curves responded to 23 fractures, with a matching rate of 92%.

[0078] This invention considers the multi-parameter response characteristics of fractured zones, including mineral composition, fracture toughness, and electrical properties, and constructs fracture strength curves. It addresses the uncertainty in calculating formation brittleness using rock mechanics parameters, as results from mineral composition alone cannot reflect the actual brittleness of the formation. Therefore, it combines fracture toughness as a weighting coefficient for each mineral in addition to mineral composition calculations, improving the accuracy of formation brittleness calculations. Furthermore, it considers the potential insensitivity of single logging parameters to fractured zones, and combines a brittleness index calculated based on mineral composition and fracture toughness to comprehensively evaluate fractures, thus improving the accuracy of fracture strength curves and consequently, the accuracy of fracture identification. After implementation in multiple well areas, the fracture strength curves obtained using this method showed a consistency rate of over 85% with imaging logging.

[0079] In summary, the present invention provides a fracture identification method based on rock mineral composition and conventional logging. This method comprehensively considers multiple parameters such as mineral composition, fracture toughness, and electrical properties of fracture-developing sections, and constructs a fracture intensity curve model. This method compensates for the insensitivity of a single logging parameter to fracture-developing sections, improves the accuracy of the fracture intensity curve, and can replace imaging logging to identify formation fractures, greatly saving measurement time and costs.

[0080] The above technical features constitute the preferred embodiment of the present invention, which has strong adaptability and optimal implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the requirements of different situations.

Claims

1. A fracture identification method based on rock mineral composition and conventional logging, characterized in that The method comprises: obtaining rock mineral component content data and logging data of a target reservoir in a research area, the logging data comprising a gamma curve, a spontaneous potential curve, an acoustic travel time curve, a neutron curve, a deep lateral resistivity, a shallow lateral resistivity, a caliper curve; determining a brittleness index of the target reservoir according to the rock mineral component; determining a fracture intensity curve of the target reservoir based on a fracture intensity curve model according to the brittleness index of the target reservoir and the logging data, wherein the fracture intensity curve model has the following expression: F = a x GR - b x SP + c x RT - d x RI + e x CNL + f x AC + g x I B - h x CAL - z Formula 1 In the formula, F is a fracture intensity curve, GR is a logging gamma value of the target reservoir, SP is a logging spontaneous potential value of the target reservoir, RT is a logging deep lateral resistivity value of the target reservoir, RI is a logging shallow lateral resistivity value of the target reservoir, CNL is a logging neutron value of the target reservoir, AC is a logging acoustic travel time value of the target reservoir, I B is a brittleness index of the target reservoir, CAL is a logging caliper value of the target reservoir, and a, b, c, d, e, f, g, h, and z are coefficients, respectively.

2. The fracture identification method based on rock mineral components and conventional logging according to claim 1, characterized in that The target reservoir is a tight formation.

3. The fracture identification method based on rock mineral components and conventional logging according to claim 1 or 2, characterized in that The rock mineral component content comprises contents of quartz, feldspar, calcite, illite, montmorillonite and chlorite.

4. The fracture identification method based on rock mineral components and conventional logging according to any one of claims 1 to 3, characterized in that The determination of the brittleness index of the target reservoir according to the rock mineral component comprises: obtaining fracture toughness data in the target reservoir according to the rock mineral component content of the target reservoir; determining the brittleness index of the target reservoir according to the fracture toughness data in the target reservoir.

5. The fracture identification method based on rock mineral composition and conventional logging according to claim 4, characterized in that The obtaining of the fracture toughness data in the target reservoir according to the rock mineral component content of the target reservoir comprises determining a comprehensive fracture toughness of the target reservoir according to the following formula, where K c is the overall fracture toughness of the target reservoir, Kci is the fracture toughness of the ith mineral, W i is the content of the ith mineral.

6. The fracture identification method based on rock mineral components and conventional logging according to claim 4 or 5, characterized in that The determination of the brittleness index of the target reservoir according to the fracture toughness data in the target reservoir comprises determining the brittleness index of the target reservoir according to the following formula, where I B is the brittleness index of the target reservoir, K Cmax is the maximum value of the integrated fracture toughness at each depth in the target reservoir, K Cmin is the minimum value of the integrated fracture toughness at each depth in the target reservoir, K c is the integrated fracture toughness of the target reservoir.

7. The fracture identification method based on rock mineral components and conventional logging according to any one of claims 1 to 6, characterized in that The method further comprises evaluating the fracture development degree according to the fracture intensity curve, comprising that the fracture development degree is positively correlated with the numerical value of the fracture intensity curve.

8. A fracture identification device based on rock mineral composition and conventional logging using the fracture identification method based on rock mineral composition and conventional logging according to any one of claims 1 to 7, characterized in that The method comprises: a data acquisition module, which obtains rock mineral component content data and logging data of a target reservoir in a research area, the logging data comprising a gamma curve, a spontaneous potential curve, an acoustic travel time curve, a neutron curve, a deep lateral resistivity, a shallow lateral resistivity RI, a caliper curve CAL; a brittleness index module, which determines a brittleness index of the target reservoir according to the rock mineral component; a fracture intensity module, which determines a fracture intensity curve of the target reservoir based on a fracture intensity curve model according to the brittleness index of the target reservoir and the logging data, wherein the fracture intensity curve model has the following expression: F = a x GR - b x SP + c x RT - d x RI + e x CNL + f x AC + g x I B - h x CAL - z Formula 1 In the formula, F is a fracture intensity curve, GR is a logging gamma value of the target reservoir, SP is a logging spontaneous potential value of the target reservoir, RT is a logging deep lateral resistivity value of the target reservoir, RI is a logging shallow lateral resistivity value of the target reservoir, CNL is a logging neutron value of the target reservoir, AC is a logging acoustic time difference value of the target reservoir, I B is a brittleness index of the target reservoir, CAL is a logging caliper value of the target reservoir, and a, b, c, d, e, f, g, h, and z are respectively coefficients.

9. The apparatus for fracture identification based on rock mineral composition and conventional logs as claimed in claim 8, wherein The method further comprises: an evaluation module, which evaluates the fracture development degree according to the numerical value of the fracture intensity curve, comprising that the fracture development degree is positively correlated with the numerical value of the fracture intensity curve.

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