CN117492080A - Shale oil soluble hydrocarbon prediction method - Google Patents
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- 150000002430 hydrocarbons Chemical class 0.000 title claims abstract description 86
- 229930195733 hydrocarbon Natural products 0.000 title claims abstract description 80
- 239000004215 Carbon black (E152) Substances 0.000 title claims abstract description 75
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000003079 shale oil Substances 0.000 title claims abstract description 26
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 39
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- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
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- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
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- G01V2210/6169—Data from specific type of measurement using well-logging
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Abstract
The invention belongs to the technical field of unconventional and new energy, and discloses a shale oil soluble hydrocarbon prediction method, which comprises the steps of S1, analyzing and obtaining a total organic carbon content sensitive logging curve, and establishing a calculation model of the soluble hydrocarbon and the total organic carbon content sensitive logging curve by combining measured localization data; s2, extracting logging data in a production work area, and importing the logging data into the calculation model to calculate soluble hydrocarbon single well data; s3, establishing a data set of seismic attribute and soluble hydrocarbon single well data, taking soluble hydrocarbon as target data, optimizing sensitive seismic attribute through intersection of the target data and the seismic attribute, performing machine learning by using a probabilistic neural network method to establish a mapping relation between the target data and the sensitive seismic attribute, and further calculating a soluble hydrocarbon target probability body on a three-dimensional seismic body to obtain a plane prediction result of the soluble hydrocarbon. The invention utilizes localization data, logging data and seismic data to realize effective prediction of soluble hydrocarbon, and provides accurate data support for shale oil dessert target optimization.
Description
Technical Field
The invention belongs to the technical field of unconventional and new energy, and particularly relates to a shale oil soluble hydrocarbon prediction method.
Background
Soluble hydrocarbons (S1) are important indicators for evaluating geological desserts of shale oil, and characterize the oil content in shale layers. For a long time, the data of the soluble hydrocarbon (S1) are obtained by experimental analysis according to drilling coring data, but the number of coring wells is small, the cost is high, and the conclusion obtained from actual measurement of the core through the data acquisition means has little guiding significance in the actual production link and the experimental deduction link, so the method has no popularization.
Current commercial software has no predictive method specifically directed to soluble hydrocarbons (S1). Related technical achievements are studied through methods such as article retrieval, patent retrieval and the like, and the fact that in addition to the fact that data of the soluble hydrocarbon (S1) are directly obtained from a rock core by adopting a data acquisition means (actually measured data of the S1 are not distributed prediction), a curve reconstruction method is generally adopted in the industry, and prediction of the soluble hydrocarbon (S1) is achieved by means of a logging curve associated with the soluble hydrocarbon (S1) and auxiliary seismic data. However, the method only extracts data at a plurality of fixed sampling points to complete the prediction of the soluble hydrocarbon (S1), the transverse change characteristics do not exist among the data, and the prediction result lacks the transverse change information of the seismic data, so the prediction result is inaccurate.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a shale oil soluble hydrocarbon prediction method so as to achieve the aim of effectively predicting soluble hydrocarbon by using localization data, logging data and seismic data.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a shale oil soluble hydrocarbon prediction method comprising the steps of:
1. analyzing and obtaining a sensitive log curve of the total organic carbon content, establishing a calculation model of the sensitive log curve of the soluble hydrocarbon and the total organic carbon content by combining measured localization data,
TOC＝a(logRT-logRT min )/(logRT max -logRT min )+b(AC-AC min )/(AC max -AC min ) 1 (1)
S1＝TOC(a 3 D 3 +a 2 D 2 +a 1 D+a 0 ) 2, 2
In formula 1, TOC is total organic carbon content, RT is resistivity, AC is acoustic time difference, a is the proportionality coefficient of RT and TOC, b is the proportionality coefficient of AC and TOC,
in the formula 2, S1 is soluble hydrocarbon, TOC is total organic carbon content, D is stratum depth, a 0 、a 1 、a 2 、a 3 Fitting coefficients for a plurality of terms respectively;
2. extracting logging data in a production work area, and leading the logging data into the single well data of the soluble hydrocarbon calculated in the formulas 1 and 2;
3. establishing a data set of seismic attribute and soluble hydrocarbon single well data, taking soluble hydrocarbon as target data, optimizing sensitive seismic attribute through intersection of the target data and the seismic attribute, performing machine learning by using a probability neural network method to establish a mapping relation between the target data and the sensitive seismic attribute, and further calculating a soluble hydrocarbon target probability body on a three-dimensional seismic body to obtain a plane prediction result of the soluble hydrocarbon.
In the third step, as a limitation of the present invention, a mapping relation formula of the target data and the sensitive seismic attribute is:
S1 target value ＝W1S1+W2S2+…+WnSn 3
In the formula 3, W1 … Wn is a weight corresponding to a high-correlation seismic attribute point of target data, and is obtained through intersection statistics between a single attribute point and the target data; s1 Target value Is a target value of soluble hydrocarbon.
As another limitation of the present invention, the analysis of the "total organic carbon content sensitive log" in the first step is obtained as follows:
and collecting and loading rock cores and logging data, establishing a junction graph, performing curve junction analysis by using the total organic carbon content and logging data of the rock core test, calculating fitting errors and fitting proportionality coefficients through polynomial fitting, and optimizing a total organic carbon content sensitive logging curve according to multi-curve fitting comparison.
As a further definition of the invention, core data includes lithology components, total organic carbon content, soluble hydrocarbon measurements; logging data includes acoustic time differences, density, resistivity, natural gamma, natural potential, and borehole diameter.
As a third limitation of the present invention, the formula of "formula 1" in the first step is fitted as follows:
and (3) preprocessing the resistivity curve and the acoustic time difference curve in the total organic carbon content sensitive logging curve, eliminating abnormal values, and establishing a fitting formula (formula 1) after curve standardization is realized.
As a further definition of the present invention, the formula fitting of "formula 2" in the step one is derived as:
according to the rock core localization analysis data, rock core sample data are selected in a depth range with a larger span, intersection analysis of soluble hydrocarbon, total organic carbon content and depth is established, a change rule of the ratio of the soluble hydrocarbon to the total organic carbon content along with the increase of the stratum depth is counted, and a ratio and depth fitting formula is established:
S1/TOC＝a 3 D 3 +a 2 D 2 +a 1 D+a 0 4. The method is to
The conversion of formula 4 gives formula 2.
As yet another limitation of the present invention, in the third step, the seismic attribute of the data set is established with the soluble hydrocarbon single well data, and the seismic attribute sample is extracted from the shale oil development area of the well bypass.
As a further definition of the present invention, the operation of extracting the seismic attribute samples is:
collecting and loading three-dimensional high-resolution processed seismic data, and carrying out fine well seismic calibration; developing high-precision seismic horizon interpretation aiming at shale oil development areas; and after the earthquake is optimized and preprocessed by utilizing the industrialized interpretation software, the seismic attribute samples of the amplitude type, the frequency type and the geometric type are extracted on the three-dimensional earthquake body by combining the interpretation results of the earthquake horizon.
By adopting the technical scheme, compared with the prior art, the invention has the following beneficial effects:
the formation of soluble hydrocarbons is directly related to two variables, total organic carbon content (TOC), which characterizes the content of organic matter in the formation, and Depth (Depth), which is a related function of temperature and pressure conditions. According to the invention, starting from single well measured data, by simulating the conversion rate of soluble hydrocarbon with total organic carbon content at different depths, a corresponding relation between the soluble hydrocarbon and the total organic carbon content and depth is established, so that single well soluble hydrocarbon prediction is realized; and then, utilizing the sensitive seismic attribute to develop a neural network to predict the plane distribution range of the soluble hydrocarbon, so as to realize the shale oil soluble hydrocarbon prediction work.
The method effectively improves single well prediction precision of the soluble hydrocarbon, further improves precision of predicting the shale oil soluble hydrocarbon by using a well logging constraint seismic inversion technology, and enhances reliability of seismic transverse inversion prediction under complex geological conditions. The method fills the blank of predicting the soluble hydrocarbon by using the geophysical method, and provides accurate data support for optimizing shale oil dessert targets.
Drawings
The invention will be described in more detail below with reference to the accompanying drawings and specific examples.
FIG. 1 is a single well integrated view of a G108-8 well in a production area; comparing the measured TOC with the calculated TOC and the measured S1 with the calculated S1 results, and confirming that the single well longitudinal TOC and S1 have high consistency;
FIG. 2 is a graph of the results of performing inversion after S1 is obtained by a single well depth fitting method; in the figure, the S1 coincidence degree at the well point is very high, the inter-well change relation accords with geological rules, and the reliability and the accuracy of the transverse prediction are proved to be high.
Detailed Description
Preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are presented for purposes of illustration and understanding only, and are not intended to limit the invention.
The embodiment provides a shale oil soluble hydrocarbon prediction method, which uses rock core, well logging and seismic data to calculate important data-soluble hydrocarbon required by comprehensive evaluation of shale oil desserts through a series of calculation steps (S1). The method specifically comprises the following steps sequentially carried out in sequence:
1. analyzing and obtaining a total organic carbon content sensitive logging curve, and establishing a calculation model of the soluble hydrocarbon and the total organic carbon content sensitive logging curve by combining measured localization data (core data).
(1) Total organic carbon content sensitive log analysis
Collecting and loading rock cores and logging data and establishing a junction graph, wherein the specific operation is that curve junction analysis is carried out on the total organic carbon content and logging data tested by the rock cores, fitting errors and fitting proportionality coefficients are calculated through polynomial fitting, and a sensitive logging curve of the total organic carbon content is optimized according to multi-curve fitting comparison;
the core data and logging data are collected and loaded by the existing method. The rock core data comprises lithology components, total organic carbon content and soluble hydrocarbon measurement results; logging data includes acoustic time differences, density, resistivity, natural gamma, natural potential, borehole diameter, etc.
(2) Fitting calculation formula for total organic carbon content
The optimized total organic carbon content sensitive logging curve is subjected to near standardization calculation, wherein the resistivity curve and the acoustic time difference curve are subjected to pretreatment, abnormal values are eliminated, and a fitting formula is established after curve standardization is realized:
TOC＝a(logRT-logRT min )/(logRT max -logRT min )+b(AC-AC min )/(AC max -AC min ) 1 (1)
Where TOC is total organic carbon content, RT is resistivity, AC is acoustic time difference, a is the ratio of RT to TOC, b is the ratio of AC to TOC.
(3) Fitting calculation of ratio (S1/TOC) fitting formula
According to the rock core localization analysis data (rock core data), rock core sample data are selected in a depth range with larger span, intersection analysis of soluble hydrocarbon content and total organic carbon content and depth is established, the change rule of the ratio (S1/TOC) of the soluble hydrocarbon content to the total organic carbon content along with the increase of stratum depth is counted, and a ratio and depth fitting formula is established:
S1/TOC＝a 3 D 3 +a 2 D 2 +a 1 D+a 0 4. The method is to
Wherein S1 is soluble hydrocarbon, TOC is total organic carbon content, D is stratum depth, a 0 、a 1 、a 2 、a 3 And fitting coefficients for multiple terms respectively.
(4) Deriving the calculation formula of soluble hydrocarbon (S1)
The above ratio formula (formula 4) is converted to obtain a soluble hydrocarbon calculation formula:
S1＝TOC(a 3 D 3 +a 2 D 2 +a 1 D+a 0 ) Formula 2.
2. The required logging data is extracted from logging data in a production work area, and is led into the above formulas 1 and 2, so that the soluble hydrocarbon single well data can be calculated.
3. Developing planar predictions of soluble hydrocarbons based on seismic data
(1) Extracting seismic attribute samples of shale oil development area of side channel of well
Collecting and loading three-dimensional high-resolution processed seismic data, and carrying out fine well seismic calibration; developing high-precision seismic horizon interpretation aiming at shale oil development areas; optimizing and preprocessing the earthquake by utilizing industrial interpretation software, and extracting an amplitude type, a frequency type and a geometric type earthquake attribute sample on a three-dimensional earthquake body by combining an earthquake horizon interpretation result;
the seismic data is collected and loaded by the existing method.
(2) And integrating the extracted seismic attribute sample with the calculated soluble hydrocarbon single well data to establish a data set.
(3) Establishing a mapping relation between target data and sensitive seismic attributes to obtain a soluble hydrocarbon plane prediction result
By means of high-quality seismic processing attribute data, soluble hydrocarbon is used as target data, sensitive seismic attributes are optimized through intersection of the target data and the seismic attributes, machine learning is conducted by using a probabilistic neural network method, and a mapping relation between the target data and the sensitive seismic attributes is established, wherein a mapping relation formula is as follows:
S1 target value =w1s1+w2s2+ … +wnsn 3
Wherein W1 … Wn is a weight corresponding to a high-correlation seismic attribute point of the target data, and is obtained through intersection statistics between a single attribute point and the target data; s1 Target value Target value for soluble hydrocarbon;
and calculating a soluble hydrocarbon target probability body on the three-dimensional seismic body by using the mapping relation between the soluble hydrocarbon target data and the sensitive seismic attribute, and extracting the probability body layer-along attribute by using the seismic horizon to obtain a plane prediction result of the soluble hydrocarbon.
It should be noted that the foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but the present invention is described in detail with reference to the foregoing embodiment, and it will be apparent to those skilled in the art that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A shale oil soluble hydrocarbon prediction method, which is characterized in that: the method comprises the following steps:
1. analyzing and obtaining a total organic carbon content sensitive well logging curve, and establishing a calculation model of the soluble hydrocarbon and the total organic carbon content sensitive well logging curve by combining measured localization data:
TOC＝a(logRT-logRT min )/(logRT max -logRT min )+b(AC-AC min )/(AC max -AC min ) 1 (1)
S1＝TOC(a 3 D 3 +a 2 D 2 +a 1 D+a 0 ) 2, 2
In formula 1, TOC is total organic carbon content, RT is resistivity, AC is acoustic time difference, a is the proportionality coefficient of RT and TOC, b is the proportionality coefficient of AC and TOC,
in the formula 2, S1 is soluble hydrocarbon, TOC is total organic carbon content, D is stratum depth, a 0 、a 1 、a 2 、a 3 Fitting coefficients for a plurality of terms respectively;
2. extracting logging data in a production work area, and leading the logging data into the single well data of the soluble hydrocarbon calculated in the formulas 1 and 2;
3. establishing a data set of seismic attribute and soluble hydrocarbon single well data, taking soluble hydrocarbon as target data, optimizing sensitive seismic attribute through intersection of the target data and the seismic attribute, performing machine learning by using a probability neural network method to establish a mapping relation between the target data and the sensitive seismic attribute, and further calculating a soluble hydrocarbon target probability body on a three-dimensional seismic body to obtain a plane prediction result of the soluble hydrocarbon.
2. A shale oil soluble hydrocarbon prediction method as claimed in claim 1, wherein: in the third step, the formula of the mapping relation between the target data and the sensitive seismic attribute is as follows:
S1 target value =w1s1+w2s2+ … +wnsn 3
In the formula 3, W1 … Wn is a weight corresponding to a high-correlation seismic attribute point of target data, and is obtained through intersection statistics between a single attribute point and the target data; s is S1 Target value Is a target value of soluble hydrocarbon.
3. A shale oil soluble hydrocarbon prediction method as claimed in claim 1 or 2, wherein: the analysis of the "total organic carbon content sensitive log" in the first step is obtained as follows:
and collecting and loading rock cores and logging data, establishing a junction graph, performing curve junction analysis by using the total organic carbon content and logging data of the rock core test, calculating fitting errors and fitting proportionality coefficients through polynomial fitting, and optimizing a total organic carbon content sensitive logging curve according to multi-curve fitting comparison.
4. A shale oil soluble hydrocarbon prediction method as claimed in claim 3, wherein: the core data comprises lithology components, total organic carbon content and soluble hydrocarbon measurement results; logging data includes acoustic time differences, density, resistivity, natural gamma, natural potential, and borehole diameter.
5. A shale oil soluble hydrocarbon prediction method as claimed in any of claims 1-2, 4, wherein: the formula fitting of "formula 1" in the first step is:
and (3) preprocessing the resistivity curve and the acoustic time difference curve in the total organic carbon content sensitive logging curve, eliminating abnormal values, and establishing a fitting formula (formula 1) after curve standardization is realized.
6. The method for predicting shale oil-soluble hydrocarbons of claim 5, wherein: the formula fitting of "formula 2" in the first step is derived as follows:
according to the rock core localization analysis data, rock core sample data are selected in a depth range with a larger span, intersection analysis of soluble hydrocarbon, total organic carbon content and depth is established, a change rule of the ratio of the soluble hydrocarbon to the total organic carbon content along with the increase of the stratum depth is counted, and a ratio and depth fitting formula is established:
S1/TOC＝a 3 D 3 +a 2 D 2 +a 1 D+a 0 4. The method is to
The conversion of formula 4 gives formula 2.
7. A shale oil soluble hydrocarbon prediction method as claimed in any of claims 1-2, 4, 6, wherein: in the third step, seismic attributes of a data set are established with the soluble hydrocarbon single-well data, and seismic attribute samples of a well bypass shale oil development area are extracted.
8. A shale oil soluble hydrocarbon prediction method as claimed in claim 7, wherein: the extraction operation of the seismic attribute sample comprises the following steps:
collecting and loading three-dimensional high-resolution processed seismic data, and carrying out fine well seismic calibration; developing high-precision seismic horizon interpretation aiming at shale oil development areas; and after the earthquake is optimized and preprocessed by utilizing the industrialized interpretation software, the seismic attribute samples of the amplitude type, the frequency type and the geometric type are extracted on the three-dimensional earthquake body by combining the interpretation results of the earthquake horizon.
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