CN117418831B - Method and device for identifying lithology of multiple logging parameters of sandstone reservoir - Google Patents
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- E21B—EARTH DRILLING, e.g. DEEP DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
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- G06F18/15—Statistical pre-processing, e.g. techniques for normalisation or restoring missing data
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Abstract
The application discloses a method and a device for identifying lithology of multiple logging parameters of a sandstone reservoir, and relates to the technical field of oil and gas field development. The method comprises the following steps: carrying out standardized treatment on the logging curve of the sandstone reservoir to obtain a standard logging curve; classifying the conglomerate reservoir to determine its lithology; selecting a corresponding standard logging curve according to lithology of the sandstone reservoir, and drawing a data intersection diagram of the selected standard logging curve; well logging identification parameters are determined based on the data intersection map, and a lithology identification map is constructed to identify lithology of the conglomerate reservoir. The lithology identification method solves the problem that the lithology identification method in the prior art cannot identify all lithologies in the sandstone reservoir, can identify lithology of all rock layers in the sandstone reservoir at one time, provides support for logging interpretation, reservoir prediction, oil reservoir evaluation, development scheme formulation and the like, and further can realize efficient exploration and development of the sandstone.
Description
Technical Field
The application relates to the technical field of oil and gas field development, in particular to a method and a device for identifying lithology of multiple logging parameters of a sandstone reservoir.
Background
Along with the continuous deep oil and gas exploration, complex strata such as sandstone become exploration key points gradually. The sand reservoir is mainly near-shore underwater fan deposition formed by collapse and carrying of sand sediment, and compared with the conventional clastic rock oil-gas reservoir, the sand reservoir has complex lithology, strong heterogeneity, different lithology rock mechanical property control factors and larger mechanical property difference; the structure of the holes of the conglomerate is complex, the types of the holes of different conglomerates are complex and various, the holes among the primary conglomerates, the dissolving holes in the secondary conglomerates and the microcracks are mixed and developed, and the holes and the microcracks form original microscopic defects, so that the mechanical characteristics of the conglomerate are complex and variable.
Unstable minerals such as feldspar in a sandstone reservoir have high content and strong radioactivity, so that gamma value distribution of the sandstone reservoir is unstable, part of the minerals are higher than mudstone (high in feldspar content), part of the minerals are lower than mudstone (high in quartz content), and the conventional gamma identification method for sand (sandstone) and mudstone is difficult to work, and great difficulty is brought to oil reservoir research.
On the other hand, the lithology of a conglomerate reservoir varies from coarse conglomerate to very fine silt sandstone, and lithology identification is difficult. Currently, a more-used lithology identification method is to draw an intersection graph according to the differences of different lithology logging parameters to identify lithology. But the simple logging curve intersection can only identify the lithology change of the conventional sandstone reservoir, can not identify all lithology in the sandstone reservoir, and has the advantages of limited logging data and low identification accuracy.
Disclosure of Invention
According to the method for identifying lithology of the sandstone reservoir with multiple logging parameters, the problem that all lithology in the sandstone reservoir cannot be identified by the lithology identification method in the prior art is solved, and the method for reliably identifying lithology of all rock layers in the sandstone reservoir is achieved.
In a first aspect, an embodiment of the present application provides a method for identifying lithology of a multiple logging parameters of a sandstone reservoir, including: carrying out standardized treatment on the logging curve of the sandstone reservoir to obtain a standard logging curve; classifying the conglomerate reservoir to determine its lithology; selecting a corresponding standard well logging curve according to lithology of a sandstone reservoir, and drawing a data intersection diagram of the selected standard well logging curve; and determining logging identification parameters based on the data intersection map, and constructing a lithology identification map to identify lithology of the conglomerate reservoir.
With reference to the first aspect, in a first possible implementation manner, the logging curves include an acoustic time difference logging curve, a natural potential logging curve, a natural gamma logging curve, a compensated neutron logging curve, a compensated density logging curve, and an induction conductivity logging curve.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the normalizing the log of the sandstone reservoir to obtain a standard log includes: determining a standard layer in a conglomerate reservoir; counting the characteristic values of the sandstone reservoir in the standard layer; drawing a data plane distribution diagram according to the characteristic values, and comparing each logging curve; and normalizing the characteristic value according to a comparison result to obtain the standard logging curve.
With reference to the first aspect, in a third possible implementation manner, the classifying the conglomerate reservoir to determine lithology thereof includes: the sandstone reservoir is divided into conglomerates, grits, conglomerate sandstones, fine sandstones, siltstone sandstones, and mudstones.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner, the selecting the corresponding standard log according to lithology of the sandstone reservoir includes: and determining at least two standard logging curves according to lithology sensitivity of the sandstone reservoir.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, the determining a logging identification parameter based on the data intersection graph, and constructing a lithology identification graph plate includes: determining lithology recognition boundaries of different lithologies according to a plurality of data intersection graphs; determining logging identification parameters according to the lithology identification limit; and constructing the lithology recognition plate according to the logging recognition parameters.
With reference to the fifth possible implementation manner of the first aspect, in a sixth possible implementation manner, the identifying lithology of the sandstone reservoir includes: determining the limit of the logging identification parameter according to the lithology identification limit; and converting the logging data of the lithology to be determined into the logging identification parameter form, inputting the obtained data points into the lithology identification plate, and determining the lithology of the sandstone reservoir according to the limit of the logging identification parameter.
In a second aspect, embodiments of the present application provide a device for identifying lithology of a plurality of logging parameters of a sandstone reservoir, including: the standardized module is used for carrying out standardized treatment on the logging curve of the sandstone reservoir to obtain a standard logging curve; a lithology determination module for classifying the conglomerate reservoir to determine lithology thereof; the drawing module is used for selecting the corresponding standard logging curve according to lithology of the sandstone reservoir and drawing a data intersection diagram of the selected standard logging curve; and the identification module is used for determining logging identification parameters based on the data intersection graph and constructing a lithology identification graph plate to identify lithology of the conglomerate reservoir.
With reference to the second aspect, in a first possible implementation manner, the logging curves include an acoustic time difference logging curve, a natural potential logging curve, a natural gamma logging curve, a compensated neutron logging curve, a compensated density logging curve, and an induction conductivity logging curve.
With reference to the first possible implementation manner of the second aspect, in a second possible implementation manner, the normalizing the log of the sandstone reservoir to obtain a standard log includes: determining a standard layer in a conglomerate reservoir; counting the characteristic values of the sandstone reservoir in the standard layer; drawing a data plane distribution diagram according to the characteristic values, and comparing each logging curve; and normalizing the characteristic value according to a comparison result to obtain the standard logging curve.
With reference to the second aspect, in a third possible implementation manner, the classifying the conglomerate reservoir to determine lithology thereof includes: the sandstone reservoir is divided into conglomerates, grits, conglomerate sandstones, fine sandstones, siltstone sandstones, and mudstones.
With reference to the third possible implementation manner of the second aspect, in a fourth possible implementation manner, the selecting the corresponding standard log according to lithology of the sandstone reservoir includes: and determining at least two standard logging curves according to lithology sensitivity of the sandstone reservoir.
With reference to the fourth possible implementation manner of the second aspect, in a fifth possible implementation manner, the determining a logging identification parameter based on the data intersection graph and constructing a lithology identification graph plate includes: determining lithology recognition boundaries of different lithologies according to a plurality of data intersection graphs; determining logging identification parameters according to the lithology identification limit; and constructing the lithology recognition plate according to the logging recognition parameters.
With reference to the fifth possible implementation manner of the second aspect, in a sixth possible implementation manner, the identifying lithology of the sandstone reservoir includes: determining the limit of the logging identification parameter according to the lithology identification limit; and converting the logging data of the lithology to be determined into the logging identification parameter form, inputting the obtained data points into the lithology identification plate, and determining the lithology of the sandstone reservoir according to the limit of the logging identification parameter.
In a third aspect, embodiments of the present application provide an apparatus, including: a processor; a memory for storing processor-executable instructions; the processor, when executing the executable instructions, implements a method as described in the first aspect or any one of the possible implementations of the first aspect.
In a fourth aspect, embodiments of the present application provide a non-transitory computer readable storage medium comprising instructions for storing a computer program or instructions which, when executed, cause a method as described in the first aspect or any one of the possible implementations of the first aspect to be implemented.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
according to the embodiment of the application, the deviation between logging data can be eliminated by standardizing the logging curve; the standard well logging curves with higher sensitivity to different lithology can be determined by selecting the corresponding standard well logging curves by lithology; by constructing a lithology recognition template, lithology of all the strata in the conglomerate reservoir can be recognized at one time. The lithology recognition method effectively solves the problem that all lithologies in the sandstone reservoir cannot be recognized by the lithology recognition method in the technology, further realizes the lithology recognition method with high reliability and strong operability, can recognize lithology of all rock strata in the sandstone reservoir at one time, provides support for logging interpretation, reservoir prediction, oil reservoir evaluation, development scheme formulation and the like, and further can realize efficient exploration and development of the sandstone.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the embodiments of the present application or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for identifying lithology of a conglomerate reservoir with multiple logging parameters according to an embodiment of the present application;
FIG. 2 is a flow chart of normalizing a log of a conglomerate reservoir to obtain a normalized log, provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a device for identifying lithology of multiple logging parameters of a conglomerate reservoir according to an embodiment of the present disclosure;
FIGS. 4a-4f are graphs of mean frequency distribution of log curves for various standard layers provided in embodiments of the present application;
FIGS. 5 a-5 e are exemplary diagrams of data intersection diagrams provided in embodiments of the present application;
FIG. 6 is an example diagram of a lithology recognition template of a conglomerate reservoir provided in an embodiment of the present application;
fig. 7 is a recognition result of a lithology recognition plate according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of a method for identifying lithology of a sandstone reservoir with multiple logging parameters according to an embodiment of the present application, including steps 101 to 104. Wherein fig. 1 is only one execution order shown in the embodiments of the present application, and does not represent the only execution order of the method for identifying lithology of a sandstone reservoir with multiple logging parameters, and the steps shown in fig. 1 may be executed in parallel or upside down, where the final result is achievable.
Step 101: and carrying out standardized treatment on the logging curve of the sandstone reservoir to obtain a standard logging curve. In the embodiment of the application, the logging curves comprise acoustic time difference logging curves, natural potential logging curves, natural gamma logging curves, compensation neutron logging curves, compensation density logging curves and induction conductivity logging curves. The acoustic time difference logging curve is used for measuring the acoustic characteristics of the rock stratum and represents the relation between the acoustic velocity and the depth of the rock stratum. The natural potential log reflects the change in the natural potential along the well profile in the formation. The natural gamma log reflects the variation of the radiation intensity of the natural gamma rays of the formation along the well profile. The compensated neutron log reflects the formation porosity values with the borehole and formation effects removed. The compensated density log reflects the density value of the rock per unit volume, measured by the irradiation source emitting atoms of high energy particles to the formation that bombard the formation. The induction conductivity log reflects the conductivity of the formation measured by the induction logging instrument.
The normalization process is performed on each log, and specific steps are shown in fig. 2, including steps 201 to 204, as follows.
Step 201: a standard layer in a conglomerate reservoir is determined. Specifically, rock stratum with stable development, similar characteristics and high recognition degree in the whole region is selected as a standard layer. The electrical characteristics of the standard layers are similar, the logging curve data have similar or approximate regular distribution, and the data are used as reference data for carrying out standardization processing on the logging curve data of the whole area.
Illustratively, the chalky system positive setting section of the depression of the river basin is taken as an example, and is a alluvial fan sediment sandstone reservoir. The lithology characteristics of the sandstone reservoir are greatly changed, the lithology is complex, and an obvious standard layer is not found in the inside of the solid section. In the repeated comparison process, a section of stable mudstone is developed at the bottom of the solid section, the morphological characteristics of the logging curve are similar, the logging curve is easy to identify, and the whole area is distributed, so the logging curve is taken as a standard layer.
Step 202: and counting the characteristic values of the sandstone reservoir in the standard stratum. In the embodiment of the application, characteristic values of a statistical acoustic time difference (AC) log, a natural potential (SP) log, a natural Gamma (GR) log, a Compensated Neutron (CNL) log, a compensated Density (DEN) log and an induced Conductivity (COND) log in a standard layer are counted. In one embodiment of the present application, the average value of each log may be used as the characteristic value.
Step 203: and drawing a data plane distribution diagram according to the characteristic values, and comparing logging curves. Specifically, a plane distribution diagram is drawn according to the characteristic values of the logging curves counted in step 202, and the data differences of the logging curves are compared. If the comparison reveals that there are outliers in each log that are well log values significantly higher or lower than the log values of the surrounding wells, then these outliers need to be normalized.
Step 204: and normalizing the characteristic value according to the comparison result to obtain a standard logging curve. In the embodiment of the application, obviously abnormal wells are found out on a plane distribution map, the histogram is made on the logging data of all normal wells, and the peak interval of the histogram is taken as a standard peak interval. And respectively making a histogram on the logging data of the abnormal well, comparing the difference between the peak interval and the standard peak interval, and translating the logging data of the abnormal well to the standard peak interval in a mode of integrally adding or subtracting the logging data of the abnormal well so as to achieve the purpose of normalizing the logging curve and obtain the standard logging curve.
By means of statistical analysis of the average value of the log curves of the standard layer of each well, the average value distribution of each log curve is concentrated, a significant main value interval exists, but few wells have abnormal values, and standardized treatment is needed.
As shown in FIGS. 4a-4f, FIG. 4a shows an acoustic time difference log, from which it can be obtained that the main value interval of the acoustic time difference is 230-260s/m. FIG. 4b is a graph of natural gamma log, where the main value interval of natural gamma is 80-110 API. FIG. 4c is a natural potential log, which is obtained from the graph, wherein the natural potential main value interval is 70-110 MV. FIG. 4d is a graph showing the induction conductivity log, from which the induction conductivity main value ranges from 120 to 240 +>s/m. FIG. 4e shows a compensated neutron log, which is obtained from the graph, wherein the compensated neutron main value interval is 20-35%. FIG. 4f is a graph showing a compensated density log, wherein the main value interval of the compensated density is 2.4-2.6->. If the log value which does not satisfy the above-mentioned corresponding main value interval is an abnormal value, it is necessary to perform normalization processing.
Step 102: the sandstone reservoir is classified to determine its lithology. Specifically, the lithology of the sandstone reservoir is complex, the granularity change is large, the dividing and classifying methods for the lithology of the sandstone reservoir are more at present, and certain differences exist in the particle size. In the embodiment of the application, according to the oil and gas industry standard clastic rock granularity analysis method (SY/T5434-2018), the lithology of a sandstone reservoir is classified and named according to the core taken out by an actual coring well and in combination with a size classification standard.
In the present embodiments, the lithology of a sandstone reservoir is divided into seven categories: conglomerate (gravel component more than 50%, coarse particle size, particle size greater than 2 mm), sand (gravel and sand content in the clastic component is less than 50%), conglomerate (gravel content in the clastic component is greater than or equal to 25% and less than 50%), conglomerate (gravel component in the middle is greater than or equal to 10% and less than 25%, sand is coarse sand and medium sand), fine sand (gravel is little or no, particle size is fine, particle size is uniform), siltstone (particle size is extremely fine, clastic particles are mainly siltstone and argillaceous), and mudstone (component is extremely fine, argillaceous particles are mainly).
Step 103: and selecting a corresponding standard logging curve according to lithology of the sandstone reservoir, and drawing a data intersection diagram of the selected standard logging curve. In an embodiment of the present application, at least two standard logs are determined from lithology sensitivity of a conglomerate reservoir. And plotting data intersection graphs with the determined standard log as vertical and horizontal axes, respectively, as shown in fig. 5a to 5 e.
In particular, there is a difference in the response of different lithology to different standard log curves. And selecting a standard logging curve with higher lithology sensitivity. In the embodiment of the application, the response of mudstone and siltstone to the natural gamma logging curve is obvious, the response of conglomerate to the compensation density logging curve is obvious, the response of granularity change to the compensation neutron logging curve is obvious, the response of conglomerate and mudstone to the natural potential logging curve is obvious, and the response of the induced conductivity to lithology is affected by the oiliness so that lithology is weaker.
Statistical analysis is performed on the logging data of different lithologies. And analyzing the intersection characteristics of the logging data of different lithologies according to the data intersection graphs of the different logging data.
Further statistical analysis of the log data shows that siltstone and mudstone can be identified by acoustic time difference (AC) log and natural Gamma (GR) log, conglomerate and sand can be identified by acoustic time difference (AC) log and compensation Density (DEN) log, conglomerate can be identified by natural Gamma (GR) log and compensation Density (DEN) log, and conglomerate and fine sandstone can be identified by Compensation Neutron (CNL) log and compensation Density (DEN) log.
Step 104: well logging identification parameters are determined based on the data intersection map, and a lithology identification map is constructed to identify lithology of the conglomerate reservoir. In an embodiment of the application, lithology recognition boundaries of rocks of different lithologies are determined according to a plurality of data intersection graphs.
In the embodiment of the present application, the data intersection constructed according to step 103, as shown in fig. 5a to 5e, takes conglomerate and sand as examples, and the sensitivity of conglomerate to acoustic time difference (AC) log and offset Density (DEN) log is higher, as shown in fig. 5 d. The lithology recognition limit of the conglomerate can be derived based on the distribution of the conglomerate in fig. 5d, i.e. the conglomerate: AC (alternating current)<250s/m，DEN>2.6/>. The sensitivity of the sand is also higher for sonic jet lag (AC) logs and offset Density (DEN) logs, as shown in fig. 5 d. The lithology recognition limit of the sandstone can be derived based on the distribution of the sandstone in fig. 5d, i.e., the sandstone: AC (alternating current)<250/>s/m，2.52g/cm3<DEN<2.60/>. And (3) selecting two standard logging curves with higher sensitivity, and establishing a data intersection graph to obtain lithology identification limits of the lithology rock in the corresponding data intersection graph. Specifically, lithology recognition limits of rocks of different lithology are as follows: conglomerate: AC (alternating current)<250/>s/m，DEN>2.6/>. Sand stone: AC (alternating current)<250/>s/m，2.52g/cm3<DEN<2.60/>. Gravel sandstone: 2.47/><DEN<2.55，CNL<22.7%. Gravel-containing sandstone: GR (glass fibre reinforced plastics)<80API，DEN<2.47/>. Fine sandstone: 2.47/><DEN<2.55，22.7%<CNL<30%. Siltstone: AC (alternating current)>280/>s/m，80API<GR<105API. Mudstone: AC (alternating current)>280/>s/m，GR>105API。
And determining logging identification parameters according to lithology identification limits. In the embodiment of the application, the influence of the positive response logging parameters is expanded through the form of power and product, the influence of the negative response logging parameters is reduced through the evolution and the ratio, and then the sandstone reservoir logging identification parameters X and Y are determined.
Specifically, the conglomerates, the gritties, the gravel-containing gritties, the conglomerate gritties and the fine gritties can be primarily identified through the compensation density, so that the logging identification parameter Y is determined by taking the compensation density as a core. In order to amplify the influence of the compensation density, logging identification parameters Y are set to be DEN, DEN,Wait, compare multiple times, find +.>The lithology recognition effect is best, so the +.>Parameter Y is identified for logging. The natural gamma can identify mudstone and siltstone, the compensating neutrons can identify conglomerate and fine sandstone, and the natural gamma and the compensating neutrons are taken as positive response parameters of logging identification parameters X. The compensation density cannot distinguish between the conglomerate and the fine sandstone mixed together, and as a negative response parameter of the logging identification parameter X, the natural potential is responsive to the conglomerate and the mudstone but cannot distinguish between the specific lithology, and as an auxiliary parameter of X, the acoustic time difference is insensitive to the response of the lithology of the reservoir of the conglomerate, so that the calculation of the logging identification parameter X is not participated. By expanding the influence of natural gamma and compensation neutrons and reducing the influence of compensation density, the logging identification parameter X can be determined by the aid of natural potential to participate in calculation. The logging identification parameters X and Y are specifically as follows:
，/>. Wherein X and Y are logging identification parameters, SP is natural potential, GR is natural gamma, CNL is compensation neutron, and DEN is compensation density.
And constructing a lithology recognition plate according to the logging recognition parameters. And drawing and identifying an intersection diagram of the logging identification parameters X and Y. And determining the limit of the logging identification parameter according to the lithology identification limit. The limits of the logging identification parameters X and Y are determined from the above-described logging data of known lithology and lithology identification limits, as shown in the limit table of the logging identification parameters of the lithology of the conglomerate reservoir as shown in table 1 below.
TABLE 1 limit table of logging identification parameters of lithology of sandstone reservoir
And converting the logging data of the lithology to be determined into a logging identification parameter form, inputting the obtained data points into a lithology identification plate, and determining the lithology of the sandstone reservoir according to the limit of the logging identification parameter. Specifically, logging identification parameters X and Y are calculated according to continuous logging curves of a single well with lithology to be determined, and finally calculated data points are cast into an intersection diagram of the logging identification parameters X and Y to form a lithology identification plate of a sandstone reservoir, as shown in fig. 6. And determining the lithology of the conglomerate reservoir in the single well according to the lithology recognition plate and the limit of the logging recognition parameters. All lithology of the sandstone reservoir can be conveniently and quickly identified at one time by using the lithology identification plate. The recognition result of the lithology recognition plate is shown in fig. 7.
Although the present application provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps recited in the present embodiment is only one way of performing the steps in a plurality of steps, and does not represent a unique order of execution. When implemented by an actual device or client product, the method of the present embodiment or the accompanying drawings may be performed sequentially or in parallel (e.g., in a parallel processor or a multithreaded environment).
As shown in fig. 3, the embodiment of the present application further provides a device 300 for identifying lithology of a sandstone reservoir with multiple logging parameters. The device comprises: the normalization module 301, the lithology determination module 302, the drawing module 303 and the identification module 304 are specifically as follows.
The normalization module 301 is configured to normalize a log of a conglomerate reservoir to obtain a standard log. The normalization module 301 is specifically used to determine a standard layer in a conglomerate reservoir. Specifically, rock stratum with stable development, similar characteristics and high recognition degree in the whole region is selected as a standard layer. The electrical characteristics of the standard layers are similar, the logging curve data have similar or approximate regular distribution, and the data are used as reference data for carrying out standardization processing on the logging curve data of the whole area.
Illustratively, the chalky system positive setting section of the depression of the river basin is taken as an example, and is a alluvial fan sediment sandstone reservoir. The lithology characteristics of the sandstone reservoir are greatly changed, the lithology is complex, and an obvious standard layer is not found in the inside of the solid section. In the repeated comparison process, a section of stable mudstone is developed at the bottom of the solid section, the morphological characteristics of the logging curve are similar, the logging curve is easy to identify, and the whole area is distributed, so the logging curve is taken as a standard layer.
And counting the characteristic values of the sandstone reservoir in the standard stratum. In the embodiment of the application, characteristic values of a statistical acoustic time difference (AC) log, a natural potential (SP) log, a natural Gamma (GR) log, a Compensated Neutron (CNL) log, a compensated Density (DEN) log and an induced Conductivity (COND) log in a standard layer are counted. In one embodiment of the present application, the average value of each log may be used as the characteristic value.
And drawing a data plane distribution diagram according to the characteristic values, and comparing logging curves. Specifically, drawing a plane distribution diagram according to the characteristic value of each well logging curve, and comparing the data difference of each well logging curve. If the comparison reveals that there are outliers in each log that are well log values significantly higher or lower than the log values of the surrounding wells, then these outliers need to be normalized.
And normalizing the characteristic value according to the comparison result to obtain a standard logging curve. In the embodiment of the application, obviously abnormal wells are found out on a plane distribution map, the histogram is made on the logging data of all normal wells, and the peak interval of the histogram is taken as a standard peak interval. And respectively making a histogram on the logging data of the abnormal well, comparing the difference between the peak interval and the standard peak interval, and translating the logging data of the abnormal well to the standard peak interval in a mode of integrally adding or subtracting the logging data of the abnormal well so as to achieve the purpose of normalizing the logging curve and obtain the standard logging curve.
By means of statistical analysis of the average value of the log curves of the standard layer of each well, the average value distribution of each log curve is concentrated, a significant main value interval exists, but few wells have abnormal values, and standardized treatment is needed.
Lithology determination module 302 is used to classify a conglomerate reservoir to determine its lithology. The lithology determining module 302 is specifically configured to make lithology of the sandstone reservoir complex, have large granularity change, and have more classification methods for lithology of the sandstone reservoir at present, and have a certain difference in particle size. In the embodiment of the application, according to the oil and gas industry standard clastic rock granularity analysis method (SY/T5434-2018), the lithology of a sandstone reservoir is classified and named according to the core taken out by an actual coring well and in combination with a size classification standard.
In the present embodiments, the lithology of a sandstone reservoir is divided into seven categories: conglomerate (gravel component more than 50%, coarse particle size, particle size greater than 2 mm), sand (gravel and sand content in the clastic component is less than 50%), conglomerate (gravel content in the clastic component is greater than or equal to 25% and less than 50%), conglomerate (gravel component in the middle is greater than or equal to 10% and less than 25%, sand is coarse sand and medium sand), fine sand (gravel is little or no, particle size is fine, particle size is uniform), siltstone (particle size is extremely fine, clastic particles are mainly siltstone and argillaceous), and mudstone (component is extremely fine, argillaceous particles are mainly).
The mapping module 303 is configured to select a corresponding standard well log according to lithology of the conglomerate reservoir, and map a data intersection of the selected standard well log. The mapping module 303 is specifically configured to determine at least two standard logs based on lithology sensitivity of the conglomerate reservoir. And respectively drawing a data intersection graph by taking the determined standard logging curves as the vertical axis and the horizontal axis.
In particular, there is a difference in the response of different lithology to different standard log curves. And selecting a standard logging curve with higher lithology sensitivity. In the embodiment of the application, the response of mudstone and siltstone to the natural gamma logging curve is obvious, the response of conglomerate to the compensation density logging curve is obvious, the response of granularity change to the compensation neutron logging curve is obvious, the response of conglomerate and mudstone to the natural potential logging curve is obvious, and the response of the induced conductivity to lithology is affected by the oiliness so that lithology is weaker.
Statistical analysis is performed on the logging data of different lithologies. And analyzing the intersection characteristics of the logging data of different lithologies according to the data intersection graphs of the different logging data.
Further statistical analysis of the log data shows that siltstone and mudstone can be identified by acoustic time difference (AC) log and natural Gamma (GR) log, conglomerate and sand can be identified by acoustic time difference (AC) log and compensation Density (DEN) log, conglomerate can be identified by natural Gamma (GR) log and compensation Density (DEN) log, and conglomerate and fine sandstone can be identified by Compensation Neutron (CNL) log and compensation Density (DEN) log.
The identification module 304 is configured to determine logging identification parameters based on the data intersection map and construct a lithology identification map to identify lithology of the conglomerate reservoir. The identification module 304 is specifically configured to determine lithology identification boundaries of different lithologies according to the plurality of data intersection graphs.
In the embodiment of the present application, the data intersection constructed according to step 103, as shown in fig. 5a to 5e, takes conglomerate and sand as examples, and the sensitivity of conglomerate to acoustic time difference (AC) log and offset Density (DEN) log is higher, as shown in fig. 5 d. The lithology recognition limit of the conglomerate can be derived based on the distribution of the conglomerate in fig. 5d, i.e. the conglomerate: AC (alternating current)<250s/m，DEN>2.6/>. The sensitivity of the sand is also higher for sonic jet lag (AC) logs and offset Density (DEN) logs, as shown in fig. 5 d. The lithology recognition limit of the sandstone can be derived based on the distribution of the sandstone in fig. 5d, i.e., the sandstone: AC (alternating current)<250/>s/m，2.52g/cm3<DEN<2.60/>. Rock of other lithologyAnd selecting two standard logging curves with higher sensitivity, and establishing a data intersection diagram so as to obtain lithology identification limits of the lithology rock in the corresponding data intersection diagram. Specifically, lithology recognition limits of rocks of different lithology are as follows: conglomerate: AC (alternating current)<250/>s/m，DEN>2.6/>. Sand stone: AC (alternating current)<250/>s/m，2.52g/cm3<DEN<2.60/>. Gravel sandstone: 2.47/><DEN<2.55，CNL<22.7%. Gravel-containing sandstone: GR (glass fibre reinforced plastics)<80API，DEN<2.47/>. Fine sandstone: 2.47/><DEN<2.55，22.7%<CNL<30%. Siltstone: AC (alternating current)>280/>s/m，80API<GR<105API. Mudstone: AC (alternating current)>280/>s/m，GR>105API。
And determining logging identification parameters according to lithology identification limits. In the embodiment of the application, the influence of the positive response logging parameters is expanded through the form of power and product, the influence of the negative response logging parameters is reduced through the evolution and the ratio, and then the sandstone reservoir logging identification parameters X and Y are determined.
Specifically, the conglomerates, the gritties, the gravel-containing gritties, the conglomerate gritties and the fine gritties can be primarily identified through the compensation density, so that the logging identification parameter Y is determined by taking the compensation density as a core. In order to amplify the influence of the compensation density, logging identification parameters Y are set to be DEN, DEN,Wait, compare multiple times, find +.>The lithology recognition effect is best, so the +.>Parameter Y is identified for logging. The natural gamma can identify mudstone and siltstone, the compensating neutrons can identify conglomerate and fine sandstone, and the natural gamma and the compensating neutrons are taken as positive response parameters of logging identification parameters X. The compensation density cannot distinguish between the conglomerate and the fine sandstone mixed together, and as a negative response parameter of the logging identification parameter X, the natural potential is responsive to the conglomerate and the mudstone but cannot distinguish between the specific lithology, and as an auxiliary parameter of X, the acoustic time difference is insensitive to the response of the lithology of the reservoir of the conglomerate, so that the calculation of the logging identification parameter X is not participated. By expanding the influence of natural gamma and compensation neutrons and reducing the influence of compensation density, the logging identification parameter X can be determined by the aid of natural potential to participate in calculation. The logging identification parameters X and Y are specifically as follows:
，/>. Wherein X and Y are logging identification parameters, SP is natural potential, GR is natural gamma, CNL is compensation neutron, and DEN is compensation density.
And constructing a lithology recognition plate according to the logging recognition parameters. And drawing and identifying an intersection diagram of the logging identification parameters X and Y. And determining the limit of the logging identification parameter according to the lithology identification limit. The limits of the logging identification parameters X and Y are determined from the above-described logging data of known lithology and lithology identification limits, as shown in the limit table of the logging identification parameters of the lithology of the conglomerate reservoir as shown in table 1 below.
TABLE 1 limit table of logging identification parameters of lithology of sandstone reservoir
And converting the logging data of the lithology to be determined into a logging identification parameter form, inputting the obtained data points into a lithology identification plate, and determining the lithology of the sandstone reservoir according to the limit of the logging identification parameter. Specifically, logging identification parameters X and Y are calculated according to continuous logging curves of a single well with lithology to be determined, and finally calculated data points are cast into an intersection diagram of the logging identification parameters X and Y to form a lithology identification plate of a sandstone reservoir, as shown in fig. 6. And determining the lithology of the conglomerate reservoir in the single well according to the lithology recognition plate and the limit of the logging recognition parameters. All lithology of the sandstone reservoir can be conveniently and quickly identified at one time by using the lithology identification plate. The recognition result of the lithology recognition plate is shown in fig. 7.
Some of the modules of the apparatus described herein may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The apparatus or module set forth in the embodiments of the application may be implemented in particular by a computer chip or entity, or by a product having a certain function. For convenience of description, the above devices are described as being functionally divided into various modules, respectively. The functions of the modules may be implemented in the same piece or pieces of software and/or hardware when implementing the embodiments of the present application. Of course, a module that implements a certain function may be implemented by a plurality of sub-modules or a combination of sub-units.
The methods, apparatus or modules described herein may be implemented in computer readable program code means and in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium storing computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (english: application Specific Integrated Circuit; abbreviated: ASIC), programmable logic controllers and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel AT91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The embodiment of the application also provides equipment, which comprises: a processor; a memory for storing processor-executable instructions; the processor, when executing the executable instructions, implements a method as described in embodiments of the present application.
The embodiments also provide a non-transitory computer readable storage medium having stored thereon a computer program or instructions which, when executed, cause a method as described in the embodiments of the present application to be implemented.
In addition, each functional module in the embodiments of the present invention may be integrated into one processing module, each module may exist alone, or two or more modules may be integrated into one module.
The storage medium includes, but is not limited to, a random access Memory (English: random Access Memory; RAM), a Read-Only Memory (ROM), a Cache Memory (English: cache), a Hard Disk (English: hard Disk Drive; HDD), or a Memory Card (English: memory Card). The memory may be used to store computer program instructions.
From the description of the embodiments above, it will be apparent to those skilled in the art that the present application may be implemented in software plus necessary hardware. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product, or may be embodied in the implementation of data migration. The computer software product may be stored on a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., comprising instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the methods described in various embodiments or portions of embodiments herein.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment is mainly described as a difference from other embodiments. All or portions of the present application can be used in a number of general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the present application; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions.
Claims (8)
1. A method for identifying lithology of a sandstone reservoir with multiple logging parameters, comprising:
carrying out standardized treatment on the logging curve of the sandstone reservoir to obtain a standard logging curve;
classifying the conglomerate reservoir to determine its lithology;
selecting a corresponding standard well logging curve according to lithology of a sandstone reservoir, and drawing a data intersection diagram of the selected standard well logging curve;
determining logging identification parameters based on the data intersection map, and constructing a lithology identification map to identify lithology of the sandstone reservoir, wherein the lithology identification map comprises the following specific steps:
determining lithology recognition boundaries of different lithologies according to a plurality of data intersection graphs;
determining logging identification parameters according to the lithology identification limit;
constructing the lithology recognition plate according to the logging recognition parameters;
determining the limit of the logging identification parameter according to the lithology identification limit;
converting logging data of lithology to be determined into a logging identification parameter form, inputting the obtained data points into the lithology identification plate, and determining lithology of a sandstone reservoir according to the limit of the logging identification parameter; wherein, the logging identification parameters are as follows:
，/>the method comprises the steps of carrying out a first treatment on the surface of the Wherein X and Y are logging identification parameters, SP is natural potential, GR is natural gamma, CNL is compensation neutron, and DEN is compensation density.
2. The method of claim 1, wherein the log comprises an acoustic moveout log, a natural potential log, a natural gamma log, a compensated neutron log, a compensated density log, and an induction conductivity log.
3. The method of claim 2, wherein normalizing the log of the conglomerate reservoir to obtain a normalized log comprises:
determining a standard layer in a conglomerate reservoir;
counting the characteristic values of the sandstone reservoir in the standard layer;
drawing a data plane distribution diagram according to the characteristic values, and comparing each logging curve;
and normalizing the characteristic value according to a comparison result to obtain the standard logging curve.
4. The method of claim 1, wherein classifying the conglomerate reservoir to determine its lithology comprises:
the sandstone reservoir is divided into conglomerates, grits, conglomerate sandstones, fine sandstones, siltstone sandstones, and mudstones.
5. The method of claim 4, wherein the selecting the corresponding standard log based on lithology of a conglomerate reservoir comprises:
and determining at least two standard logging curves according to lithology sensitivity of the sandstone reservoir.
6. A device for identifying lithology of a plurality of logging parameters of a sandstone reservoir, comprising:
the standardized module is used for carrying out standardized treatment on the logging curve of the sandstone reservoir to obtain a standard logging curve;
a lithology determination module for classifying the conglomerate reservoir to determine lithology thereof;
the drawing module is used for selecting the corresponding standard logging curve according to lithology of the sandstone reservoir and drawing a data intersection diagram of the selected standard logging curve;
the identification module is used for determining logging identification parameters based on the data intersection map, and constructing a lithology identification map plate to identify lithology of the sandstone reservoir, and specifically comprises the following steps:
determining lithology recognition boundaries of different lithologies according to a plurality of data intersection graphs;
determining logging identification parameters according to the lithology identification limit;
constructing the lithology recognition plate according to the logging recognition parameters;
determining the limit of the logging identification parameter according to the lithology identification limit;
converting logging data of lithology to be determined into a logging identification parameter form, inputting the obtained data points into the lithology identification plate, and determining lithology of a sandstone reservoir according to the limit of the logging identification parameter; wherein, the logging identification parameters are as follows:
，/>the method comprises the steps of carrying out a first treatment on the surface of the Wherein X and Y are logging identification parameters, SP is natural potential, GR is natural gamma, CNL is compensation neutron, and DEN is compensation density.
7. An apparatus for performing a method of lithology identification of a multiple logging parameter of a conglomerate reservoir, comprising:
a processor;
a memory for storing processor-executable instructions;
the processor, when executing the executable instructions, implements the method of any one of claims 1 to 5.
8. A non-transitory computer readable storage medium comprising instructions for storing a computer program or instructions which, when executed, cause the method of any one of claims 1 to 5 to be implemented.
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