Complex shale strata lithology identification method based on logging information

Abstract

The invention relates to the technical field of oil exploration, in particular to a complex shale strata lithology identification method based on logging information. The method comprises the following steps: determining lithology and classification of a coring sample through experimental means: mudstone, shale, siltstone, silty mudstone and marl-containing rock; logging curve response characteristics of different lithology in the shale bed series are extracted according to the lithology analysis result, and an average value is calculated; and performing fitting processing on a logging response parameter mean value or a parameter value after standardization of a mean value range, amplifying logging curve response characteristics of different lithologies, and further performing effective identification. According to the method for identifying the lithology of the complex shale bed series based on the logging information provided by the invention, aiming at the complex shale bed series, various lithology identification in the complex shale bed series can be accurately completed, so that the dessert section of the shale oil and gas reservoir is optimized, and the identification accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and particularly relates to a method for identifying lithology of complex shale formations based on logging data. The present invention relates to the technical field of oil exploration, and particularly relates to a method for identifying lithology of complex shale formations based on logging data. Background Art

[0002] The energy issue is related to national security, social stability and sustainable development. Shale oil and gas, as an unconventional resource, can serve as an important force to stabilize China's oil and gas production to cope with the increasing external dependence on oil and gas in China.

[0003] As the reservoir space of shale oil and gas, shale formations are one of the key bases for the enrichment of shale oil and gas. However, the lithology of continental shale formations in China is complex and highly heterogeneous. For example, the Jurassic shale formations in the Sichuan Basin are developed with four main lithologies: mudstone, shale, siltstone and muddy limestone. Different lithologies or lithology combinations determine the differences in oil and gas reservoir spaces. Therefore, it is necessary to have a clear understanding of the lithology in shale formations.

[0004] In current lithology identification research, it mainly includes direct identification methods (hand specimens, thin sections) and experimental analysis methods (element testing, mineral testing), etc. Among them, the direct identification method is mostly affected by the subjectivity of the describers, resulting in lithology discrimination errors. In contrast, the experimental analysis method has higher accuracy and more convincing identification results. However, the experimental method is easily restricted by samples. Therefore, how to conduct lithology identification and evaluation of the entire section and even the entire area of shale formations based on the existing part of lithology identification by the experimental method has become a key issue. Compared with the whole-area collection of rock samples, it is easier to obtain logging data for the whole area. Therefore, it is of great significance to clarify a method for accurately identifying lithology in complex shale formations based on easily obtained and highly operable logging data. Summary of the Invention

[0005] (I) Technical Problems to be Solved The present invention provides a method for identifying lithology of complex shale formations based on logging data to solve the problem that the existing technology fails to accurately identify different lithologies in shale formations, so as to further highlight the differences in physical properties, oil-bearing properties and brittleness of different lithologies, and thus conduct sweet spot section optimization.

[0006] (II) Technical Solutions To solve the above problems, the present invention provides a method for identifying lithology of complex shale formations based on logging data, including: Step S1: Determine the lithology and classification of core samples through experimental means: mudstone, shale, siltstone, silty mudstone and muddy limestone; Step S2: Extract the logging curve response characteristics of different lithologies in the shale formation according to the lithology analysis results and calculate the average value; Step S3: Fit the mean value of the logging response parameters or the parameter values after normalizing the mean range, amplify the logging curve response characteristics of different lithologies, and then conduct effective identification.

[0007] Preferably, the experimental means include hand specimen description, whole rock mineral analysis or thin section observation.

[0008] Preferably, the parameters with obvious logging response characteristics for the lithology of complex shale formations include: acoustic wave AC, density DEN, natural gamma ray GR, resistivity RT, and neutron CNL.

[0009] Preferably, Step S3 includes: For the complex shale formation, the parameters with obvious response characteristics for shale are density DEN and acoustic wave AC. Specifically, it includes: determining that the average value of the DEN value of shale lithology is a high value, and the average value of the AC value is a low value. F(DEN - AC) is the amplification and reduction of the normalized DEN value and AC value of the range. Wherein: F(DEN - AC) = (DEN - DEN min ) / (DEN max - DEN min ) - (AC - AC min ) / (AC max - AC min ); Make a crossplot of the AC and F(DEN - AC) data, and the shale is distributed in the upper left region of the crossplot.

[0010] Preferably, Step S3 further includes: For the complex shale formation, the parameters with obvious response characteristics for marlstone and siltstone lithologies are natural gamma ray GR and resistivity RT. Specifically, it includes: determining that the average values of GR and RT of marlstone and siltstone lithologies are high values, and the two lithologies can be distinguished in different regions of the GR and RT crossplot. Make a crossplot of the GR and RT data, and the siltstone and marlstone are respectively distributed in the lower right region of the crossplot.

[0011] Preferably, Step S3 further includes: For the complex shale formation, the parameters with obvious response characteristics for siltstone mudstone and mudstone lithologies are acoustic wave AC and neutron CNL. Specifically, it includes: determining that the average value of the CNL value of siltstone mudstone is a low value, the average value of the AC value is a low value, the average value of the CNL value of mudstone is a high value, and the average value of the AC value is a high value. Wherein: F(AC - CNL) is to amplify the characteristics of the high - value response of mudstone after normalization. F(AC - CNL) = (AC - AC min ) / (AC max - AC min) + (CNL - CNL min ) / (CNL max - CNL min ); Make a crossplot of F(CNL - AC) and CNL data, and the mudstone is distributed in the upper right region of the crossplot.

[0012] (III) Advantageous Effects The present invention provides a method for identifying lithology of complex shale formations based on well logging data. For complex shale formations, it can accurately identify various lithologies in complex shale formations, so as to further highlight the differences in the "seven properties (geochemical properties, physical properties, oil-bearing properties, brittleness, and in-situ stress properties, etc.)" of different lithologies, thereby optimizing the sweet spots of shale oil and gas reservoirs and improving the identification accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 is a flow chart of the method for identifying lithology of complex shale formations based on well logging data according to an embodiment of the present invention; Figure 2 is the logging response characteristics of different lithologies in the shale formation in an embodiment of the present invention; Figure 3 is the lithology identification chart of shale in an embodiment of the present invention; Figure 4 is the lithology identification chart of siltstone and muddy limestone in an embodiment of the present invention; Figure 5 is the lithology identification chart of mudstone and silty mudstone in an embodiment of the present invention. Embodiments

[0014] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0015] As Figures 1-5 shown, the present invention provides a method for identifying lithology of complex shale formations based on well logging data, including the following steps: Step S1: Based on experiments such as hand specimen description, whole-rock mineral analysis (XRD), and thin section observation, clarify the lithology and classification of the core samples in Region X of the Sichuan Basin, totaling 5 types: mudstone, shale, siltstone, silty mudstone, and muddy limestone.

[0016] Step S2: Extract the logging curve response characteristics of different lithologies in the shale series according to the lithology analysis results and calculate the average values. The parameters with obvious logging response characteristics for the shale series lithology include: acoustic wave AC, density DEN, natural gamma ray GR, resistivity RT, and neutron CNL. See Figure 2 a, 2b, and 2c. Figure 2 In order to show the logging curve characteristics that can distinguish each lithology in each figure, for example Figure 2 (a) The shale lithology indicated by z can be distinguished by AC and DEN, corresponding to Figure 3 each other.

[0017] Step S3: Fit the logging response parameters or the parameter values after range normalization to amplify the logging curve response characteristics of different lithologies.

[0018] This step specifically includes: Among them, for the parameters with obvious shale response characteristics in the complex shale series are DEN and AC. Specifically, it includes: determining that the average value of the DEN value of the shale lithology is a high value, and the average value of the AC value is a low value. F(DEN - AC) is the amplification and reduction of the range-normalized DEN value and AC value. F(DEN - AC) = (DEN - DEN min ) / (DEN max - DEN min ) - (AC - AC min ) / (AC max - AC min ). Make a crossplot of the AC and F(DEN - AC) data. The shale is distributed in the upper left area of the crossplot ( Figure 3 ).

[0019] Among them, for the parameters with obvious response characteristics of marlstone and siltstone lithologies in the complex shale series are GR and RT. Specifically, it includes: determining that the average values of GR and RT of the marlstone and siltstone lithologies are high values, and the two lithologies can be distinguished in different areas in the GR and RT crossplot. Make a crossplot of the GR and RT data. The siltstone and marlstone are respectively distributed in the lower right area of the crossplot ( Figure 4 ).

[0020] Among them, for the parameters with obvious response characteristics of siltstone shale and shale lithologies in the complex shale series are AC and CNL. Specifically, it includes: determining that the average value of the CNL value of the siltstone shale is a low value and the average value of the AC value is a low value, the average value of the CNL value of the shale is a high value, and the average value of the AC value is a high value. F(AC - CNL) is the amplification of the characteristics after normalizing the high-value response of the shale. F(AC - CNL) = (AC - AC min ) / (AC max - AC min ) + (CNL - CNL min) / (CNL max -CNL min ), making a crossplot of F(CNL-AC) and CNL data, and the mudstone is distributed in the upper right region of the crossplot ( Figure 5 ).

[0021] The lithology identification method for complex shale formations based on logging data provided by the present invention can accurately identify various lithologies in complex shale formations for complex shale formations, so as to further highlight the differences in the "seven properties (geochemical properties, physical properties, oil-bearing properties, brittleness, and in-situ stress properties, etc.)" of different lithologies, thereby optimizing the sweet spots of shale oil and gas reservoirs and improving the identification accuracy.

[0022] The above embodiments are only used to illustrate the present invention and are not intended to limit the present invention. Those of ordinary skill in the relevant technical fields can also make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, all equivalent technical solutions also belong to the scope of the present invention, and the patent protection scope of the present invention shall be defined by the claims.

Claims

1. A method for lithology identification of complex shale formations based on well logging data, characterized in that, Including: Step S1: Identify the lithology and classification of the core samples through experimental means: mudstone, shale, siltstone, silty mudstone, and muddy limestone; Step S2: Extract the logging curve response characteristics of different lithologies in the shale formation based on the lithology analysis results and calculate the average values; Step S3: Perform fitting processing on the mean value of the logging response parameters or the parameter values after normalizing the range of the mean values to amplify the logging curve response characteristics of different lithologies, and then conduct effective identification.

2. The method for lithology identification of complex shale formations based on well logging data according to claim 1, characterized in that, The experimental means include hand specimen description, whole-rock mineral analysis, or thin section observation.

3. The method for lithology identification of complex shale formations based on well logging data according to claim 1, characterized in that, The parameters with obvious logging response characteristics for the lithology of the complex shale formation include: acoustic wave AC, density DEN, natural gamma ray GR, resistivity RT, and neutron CNL.

4. The method for lithology identification of complex shale formations based on well logging data according to claim 3, characterized in that, Step S3 includes: For the parameters with obvious shale response characteristics in the complex shale formation, which are density DEN and acoustic wave AC, specifically including: determining that the average value of the DEN value of the shale lithology is a high value, and the average value of the AC value is a low value. F(DEN - AC) is the amplification and reduction of the DEN value and the AC value after normalizing the range. Among them: F(DEN - AC)=(DEN - DEN min ) / (DEN max - DEN min )-(AC - AC min ) / (AC max - AC min ); Make a crossplot of the AC and F(DEN - AC) data, and the shale is distributed in the upper left region of the crossplot.

5. The method for lithology identification of complex shale formations based on well logging data according to claim 3, characterized in that, Step S3 also includes: For the parameters with obvious lithology response characteristics of muddy limestone and siltstone in the complex shale formation, which are natural gamma ray GR and resistivity RT, specifically including: determining that the average values of GR and RT of the muddy limestone and siltstone lithologies are high values, and the two lithologies are distinguished in different regions in the GR - RT crossplot. Make a crossplot of the GR and RT data, and the siltstone and muddy limestone are respectively distributed in the lower right region of the crossplot.

6. The method for lithology identification of complex shale formations based on well logging data according to claim 3, characterized in that, Step S3 also includes: For the parameters with obvious lithology response characteristics of silty mudstone and mudstone in the complex shale formation, which are acoustic wave AC and neutron CNL, specifically including: determining that the average value of the CNL value of the silty mudstone is a low value, the average value of the AC value is a low value, the average value of the CNL value of the mudstone is a high value, and the average value of the AC value is a high value. Among them: F(AC - CNL) is to amplify the characteristics after normalizing the high-value response of the mudstone. F(AC - CNL)=(AC - AC min ) / (AC max - AC min )+(CNL - CNL min ) / (CNL max - CNL min ); Make a crossplot of the F(CNL - AC) and CNL data, and the mudstone is distributed in the upper right region of the crossplot.

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