CN115788418A - Unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis - Google Patents
Unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis Download PDFInfo
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Abstract
The invention relates to the technical field of unconventional oil and gas exploration and development, in particular to a method for finely evaluating unconventional oil and gas reservoirs based on logging multi-parameter comprehensive analysis. The method comprises the steps of obtaining logging information of a well to be evaluated, and determining a shale layer section to be evaluated; reading the interpretation parameters of the shale layer section of the well to be evaluated, and preliminarily interpreting the reservoir layer according to the work area interpretation standard; selecting an explanation parameter and a preliminary explanation conclusion, and establishing a reservoir grading index model; judging the correlation between the interpretation parameters and the preliminary interpretation conclusion; calculating a reservoir grading index CCFJ by using a reservoir grading index model, and determining the reservoir category according to the size of the reservoir grading index CCFJ; and outputting a fine evaluation result diagram of the shale interval to be evaluated. The method provides a basis for dynamic layer selection of the horizontal well target frame, meets the requirement of fine evaluation of the reservoir and reduces the comprehensive cost of exploration and development.
Description
Technical Field
The invention relates to the technical field of unconventional oil and gas exploration and development, in particular to a method for finely evaluating an unconventional oil and gas reservoir based on logging multi-parameter comprehensive analysis.
Background
Reservoir evaluation is one of the key technical contents in unconventional oil and gas exploration and development. The unconventional reservoir evaluation is carried out by utilizing logging information, the characteristics and the mutual matching relation of interpretation parameters such as lithology, physical property, oil-containing property, hydrocarbon source rock physical property, brittleness and the like of a shale reservoir are revealed, a stratum selection basis is provided for horizontal well development, and the method has important significance for guiding horizontal well drilling and efficiently developing shale gas resources.
With the commercial development of the marine shale layer system of the Longmaxi group of the Ching system of Ordovician province and the Wufeng group of the Odoku system of the Shixue system of the Sichuan basin, shale oil and gas resources of the Jurashijiang group, the Daanzhai section and the Dongye temple section of the Jumbo system in the basin are increasingly concerned. Practice proves that indexes such as native quality, storage performance and compressibility of the marine facies and continental facies shale reservoirs show certain difference, and the evaluation method established by the marine facies shale reservoir is not completely applicable to continental facies shale reservoir evaluation, so that the oil and gas reservoir law is not known, the reservoir interpretation effect is not ideal, and great challenges are brought to the unconventional reservoir logging fine evaluation.
The existing unconventional reservoir evaluation method generally divides evaluation indexes into two categories of geological parameters and engineering parameters, and determines related evaluation standards according to a gas test result and a region selection standard; however, the tabulated evaluation standard cannot effectively reflect the matching relation between reservoir interpretation parameters, and the reservoir evaluation parameters are compared in a general qualitative mode according to all parameters to give out a comprehensive qualitative judgment result, so that quantitative grading cannot be carried out, and the problems of artificial misjudgment and misjudgment are caused when the interpretation parameters are mutually contradictory. Therefore, a shale reservoir fine evaluation method based on logging multi-parameter constraint conditions needs to be established, and technical support is provided for efficient development of shale oil and gas resources.
CN104239703A discloses an evaluation method relating to shale gas multi-parameter quantitative analogy, which is characterized in that a multi-parameter analogy chart, a multi-parameter quantitative analogy correlation judgment chart, an analogy index Ia-correlation strength R are drawn according to the interpretation parameters of a target interval of a well to be evaluated and a typical shale gas layer of a tested gas well 2 Evaluating the cross plot, comparing the analog evaluation index Ia-analog correlation strength R of the target layer to be explained 2 Data points are placed at an analog evaluation index Ia-analog correlation intensity R 2 And (5) rendezvousing the plates, and finally outputting an evaluation result. The method provides a reliable technical means for shale reservoir multi-parameter quantitative evaluation, but the method divides shale interval interpretation results into a gas layer and a gas-bearing layer, is more suitable for exploratory well evaluation and is not suitable for unconventional oil and gas reservoir evaluation. Moreover, the interpretation requires more data, the calculation method and the interpretation steps are relatively complex, and the difficulty in field application is relatively high.
Disclosure of Invention
The invention aims to provide an unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis aiming at the defects of the prior art, provides a basis for dynamic layer selection of a horizontal well target frame, meets the requirement of reservoir fine evaluation and reduces the comprehensive exploration and development cost.
The invention provides a logging multi-parameter comprehensive analysis-based unconventional oil and gas reservoir fine evaluation method, which comprises the following steps
Acquiring logging information of a well to be evaluated, and determining a shale interval to be evaluated;
reading the interpretation parameters of the shale layer section of the well to be evaluated, and preliminarily interpreting the reservoir layer according to the work area interpretation standard;
selecting an explanation parameter and a preliminary explanation conclusion, and establishing a reservoir classification index model;
judging the correlation between the interpretation parameters and the preliminary interpretation conclusion;
calculating a reservoir grading index CCFJ by using a reservoir grading index model, and determining the reservoir category according to the size of the reservoir grading index CCFJ;
and outputting a fine evaluation result diagram of the shale interval to be evaluated.
Preferably, the obtaining of logging information of the well to be evaluated and the determining of the shale interval to be evaluated include:
acquiring logging and logging information to be evaluated, wherein the logging and logging information comprises a horizon, a well section, lithology, ZS during drilling, natural gamma GR, total hydrocarbons Ct, methane JW, total Organic Carbon (TOC), porosity POR, gas content Q and brittleness index BI;
and determining the shale interval to be evaluated according to the lithology, the thickness H, the Ct of the total hydrocarbon, the TOC of the total organic carbon content and the POR interpretation parameter change of the well to be evaluated.
Preferably, the determining the shale interval to be evaluated includes:
selecting a shale interval meeting the following conditions as a shale interval to be evaluated;
the conditions comprise that the lithology is shale, the continuous effective thickness is not less than 15.0m, the Ct anomaly display of the total hydrocarbon is shown, the hydrocarbon ratio KC is not less than 2, the TOC content of the total organic carbon of the sea-phase shale reservoir is not less than 1.0%, the TOC content of the total organic carbon of the continental-phase shale reservoir is not less than 0.5%, and the hydrocarbon ratio KC is the ratio of the total hydrocarbon anomaly value to the base value.
Preferably, the reading of the interpretation parameters of the shale interval of the well to be evaluated and the preliminary interpretation of the reservoir according to the work area interpretation standard include:
reading interpretation parameters including a total hydrocarbon Ct, a hydrocarbon ratio KC, a drilling time ratio ROPn/s, total organic carbon content TOC, porosity POR, gas saturation Sg and brittleness index BI according to the depth corresponding relation;
aiming at different regions and different exploration strata, according to different reservoir types of marine facies and continental facies, adopting corresponding logging interpretation evaluation standards, interpreting shale layer sections layer by taking step length as a unit according to depth correspondence, and interpreting conclusions to be I-class shale reservoir, II-class shale reservoir and III-class shale reservoir.
Preferably, when the interpretation parameter combinations are mutually contradictory, four interpretation parameters of total organic carbon content TOC, brittleness index BI, porosity POR and total hydrocarbon Ct are selected according to tested well data in the area, and the interpretation results are given after all the parameters are compared according to the preferred sequence of the total organic carbon content TOC, the brittleness index BI, the porosity POR and the total hydrocarbon Ct.
Preferably, the selecting the explanation parameters and the preliminary explanation conclusion, and the establishing the reservoir classification index model comprises:
selecting 3-4 interpretation parameters from the total hydrocarbon Ct, the total organic carbon content TOC, the porosity POR and the brittleness index BI according to logging information and regional characteristics, and reading a preliminary interpretation conclusion of the depth corresponding to the interpretation parameters;
carrying out standardization processing on the preliminary interpretation conclusion of the reservoir, so that the reservoir of the shale of class I =1, the reservoir of the shale of class II =2 and the reservoir of the shale of class III =3;
solving an interpretation parameter influence coefficient and a correction constant by utilizing multi-parameter linear regression analysis;
performing multiple linear regression analysis by using the initial interpretation conclusion after the standardization treatment and the interpretation parameters of the corresponding depth to obtain an interpretation parameter influence coefficient and a correction constant B;
and establishing a reservoir grading index model according to the interpretation parameter influence coefficients and the correction constant B, wherein the reservoir grading index CCFJ of the reservoir grading index model is obtained by adding the sum of the products of each interpretation parameter and the corresponding interpretation parameter influence coefficient and the correction constant B.
Preferably, the judging and interpreting parameter and the preliminary interpretation conclusion are correlated by:
if the multi-parameter linear regression correlation coefficient R is more than or equal to 0.7, the characterization interpretation conclusion and the selected interpretation parameter are in positive correlation relationship, and the next step is carried out;
if the multi-parameter linear regression correlation coefficient R is less than 0.7, the correlation between the characterization interpretation conclusion and the interpretation parameter is weak, the previous step is returned, and the interpretation parameter and the preliminary interpretation conclusion are optimized again for regression analysis.
Preferably, the calculating the reservoir classification index CCFJ by using the reservoir classification index model and the determining the reservoir category according to the size of the reservoir classification index CCFJ includes:
substituting the interpretation parameters including the total hydrocarbon Ct, the total organic carbon content TOC, the porosity POR and the brittleness index BI into the reservoir grading index model, and calculating the reservoir grading index CCFJ of the corresponding depth;
and determining the reservoir categories according to the size of the reservoir grading index CCFJ, wherein the categories comprise a category I shale reservoir, a category II shale reservoir, a category III shale reservoir and a non-effective shale reservoir.
Preferably, the determining the reservoir category according to the size of the reservoir ranking index CCFJ includes:
when the reservoir classification index CCFJ is less than or equal to 1.5, the reservoir is a shale reservoir of type I;
when the reservoir classification index CCFJ is more than 1.5 and less than or equal to 2.5, the reservoir is a type II shale reservoir;
when the reservoir classification index CCFJ is more than 2.5 and less than or equal to 3.5, the reservoir is a III-class shale reservoir;
and when the reservoir grading index CCFJ is more than 3.5, the reservoir is a non-effective shale reservoir.
Preferably, the outputting of the fine evaluation result map of the shale interval to be evaluated includes:
outputting a fine evaluation result diagram of the shale interval reservoir to be evaluated according to user requirements, wherein the fine evaluation result diagram comprises a horizon, a natural gamma GR, a ZS (zero-crossing-over) at the drilling time, a depth, a lithology, a total hydrocarbon Ct, a methane JW, a total organic carbon content TOC, a porosity POR, a brittleness index BI, a reservoir grading index CCFJ, a preliminary explanation, a fine evaluation and other diagrams, and characterizing reservoir explanation parameters and an evaluation result.
The invention has the beneficial effects that:
1. the method comprehensively considers the comprehensive influence of various factors such as oil-gas content, hydrocarbon source characteristics, physical properties, brittleness and the like of the reservoir on the reservoir, and fully excavates the characterization values of geological parameters and engineering parameters by various indexes; the correlation between the multiple interpretation parameters and the corresponding depth preliminary interpretation conclusion is utilized to establish a multi-parameter reservoir classification index model, so that the matching relation between the reservoir interpretation parameters is effectively represented, the reservoir classification is quantized, the problems of ambiguity and even contradiction of the interpretation conclusion of the traditional evaluation method are solved, and the reliability of the interpretation conclusion is greatly improved.
2. The method has the advantages of easily obtained required parameters, simple operation of interpreters, wide applicability, capability of meeting the requirements of rapid and fine evaluation of unconventional reservoirs such as marine facies shale gas and continental facies shale gas in China and worthy of popularization.
3. The method applies 36 wells in FL gas fields and peripheral areas of Sichuan basin, is suitable for evaluating reservoir layers of shale strata of marine facies and continental facies, obtains high-yield industrial airflow from evaluated I-type shale reservoir layers through large-scale fracturing gas testing of horizontal wells, meets the requirements of fine evaluation of logging shale reservoir layers and dynamic layer selection of target frames of the horizontal wells, has good popularization and application values, and improves the logging evaluation and engineering service level of the shale reservoir layers in China.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a diagram of a fine evaluation achievement of the shale layer interval reservoir applied to the Szechwan basin FL gas field sea phase A well;
FIG. 3 is a diagram of the fine evaluation achievement of the reservoir applied to the shale interval reservoir of the FL gas field continental facies B well in the Szechwan basin.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise. "plurality" means "two or more".
Example one
Fig. 1 is a schematic flow chart of a method for fine evaluation of an unconventional hydrocarbon reservoir based on logging multi-parameter comprehensive analysis according to a preferred embodiment of the present application, and for convenience of description, only the parts related to this embodiment are shown, and detailed descriptions are as follows:
step 1, obtaining logging information of a well to be evaluated, and determining a shale interval to be evaluated
101, acquiring logging and logging information to be evaluated, wherein the logging and logging information comprises a horizon, a well section, lithology, a drilling time (ZS), a natural Gamma Ray (GR), a total hydrocarbon (Ct), methane (JW), a total organic carbon content (TOC), a Porosity (POR), a gas content (Q), a Brittleness Index (BI) and the like;
102, determining a shale interval to be evaluated according to changes of interpretation parameters such as lithology, thickness (H), total hydrocarbon (Ct), total organic carbon content (TOC), porosity (POR) and the like of a well to be evaluated;
the shale layer interval to be evaluated judging method comprises the following steps: the lithology is shale, the continuous effective thickness is not less than 15.0m, the total hydrocarbon (Ct) is abnormally displayed, the hydrocarbon ratio (KC) is not less than 2, the total organic carbon content (TOC) of the sea-phase shale reservoir is not less than 1.0%, and the total organic carbon content (TOC) of the continental-phase shale reservoir is not less than 0.5%; the hydrocarbon ratio (KC) is the ratio of the total hydrocarbon (Ct) outlier to the base;
step 2, reading shale interval interpretation parameters to be evaluated, and preliminarily interpreting the reservoir according to the work area interpretation standard
Step 201, reading interpretation parameters of shale intervals of a well to be evaluated
Reading interpretation parameters such as total hydrocarbons (Ct), a hydrocarbon ratio (KC), a drilling time ratio (ROPn/s), total organic carbon content (TOC), porosity (POR), gas saturation (Sg), brittleness Index (BI) and the like according to the depth corresponding relation;
the interpretation parameters are taken according to a certain step length, generally, the values are continuously taken according to the step length of 1m, and the interpretation parameter values are average values in the step length;
step 202, initially interpreting the reservoir according to the regional interpretation criteria
Aiming at different regions and different exploration strata, according to reservoir types with different sea facies and continental facies, adopting corresponding well logging interpretation evaluation standards, interpreting shale layer sections layer by layer in a step length unit according to a depth corresponding relation, and interpreting the reservoir types to obtain interpretation results that the shale reservoir types are I shale reservoir, II shale reservoir and III shale reservoir, when the interpretation parameter combinations have obvious mutual contradiction, selecting 4 interpretation parameters of total organic carbon content (TOC), brittleness Index (BI) and porosity (POR and total hydrocarbon (Ct) according to tested gas well information in the region, and comparing the parameters according to the optimal sequence of the total organic carbon content (TOC) > Brittleness Index (BI) > Porosity (POR) > total hydrocarbon (Ct) to obtain an interpretation result;
the method comprises the following steps of (1) measuring and recording well interpretation evaluation criteria and parameter combinations (see table 1) of a Szechwan basin FL gas field marine facies shale reservoir, and measuring and recording well interpretation evaluation criteria and parameter combinations (see table 2) of a continental facies shale reservoir;
TABLE 1 Szechwan basin FL gas field sea shale reservoir logging evaluation criteria
TABLE 2 Szechwan basin FL gas field continental facies shale reservoir logging evaluation criteria
Step 301, selecting interpretation parameters according to logging information and regional characteristics, wherein the optimal interpretation parameters are 3 or 4, and all hydrocarbons (Ct), total organic carbon content (TOC), porosity (POR) and Brittleness Index (BI) are selected from FL gas field marine facies and continental facies shale reservoirs in the Sichuan basin; reading a preliminary interpretation conclusion of the depth corresponding to the interpretation parameters;
step 302, conducting standardization processing on the reservoir preliminary explanation conclusion to enable the reservoir of shale of class I =1, the reservoir of shale of class II =2 and the reservoir of shale of class III =3;
303, solving an influence coefficient and a correction constant of an interpretation parameter by utilizing multi-parameter linear regression analysis;
performing multiple linear regression analysis by using the normalized initial interpretation conclusion and the interpretation parameters with corresponding depths to obtainTaking the influence coefficient of the interpretation parameter (A) Ct 、A TOC 、A POR 、A BI ……A n ) And a correction constant B; taking 4 interpretation parameter regression analyses of selected total hydrocarbon (Ct), total organic carbon content (TOC), porosity (POR) and Brittleness Index (BI) as an example, the reservoir classification index model is as follows:
CCFJ＝A Ct ×Ct+A TOC ×TOC+A POR ×POR+A BI ×BI+B；
in the formula, A Ct 、A TOC 、A POR 、A BI The influence coefficient, the correction constant B and the reservoir grading index CCFJ are respectively expressed by decimal numbers; ct, TOC, POR, BI are all expressed in%; all parameters are dimensionless;
step 4, judging the correlation between the explanation parameters and the preliminary explanation conclusion
The multi-parameter linear regression correlation coefficient R is more than or equal to 0.7, the characterization interpretation conclusion and the selected interpretation parameter are in positive correlation, the next step is carried out, R is less than 0.7, the correlation between the characterization interpretation conclusion and the reservoir interpretation parameter is weak, the step 3 is returned, and the reservoir interpretation parameter and the preliminary interpretation conclusion are re-optimized for regression analysis;
Step 501, substituting interpretation parameters including total hydrocarbons (Ct), total organic carbon content (TOC), porosity (POR) and Brittleness Index (BI) into a reservoir fine evaluation model, and calculating a reservoir grading index (CCFJ) of a corresponding depth;
step 502, determining a reservoir category according to the size of the reservoir rating index (CCFJ): the shale reservoir stratum of the I category has a reservoir stratum grading index (CCFJ) of less than or equal to 1.5; a class II shale reservoir, wherein the reservoir classification index (CCFJ) is more than 1.5 and less than or equal to 2.5; a class III shale reservoir, wherein the reservoir grading index (CCFJ) is more than 2.5 and less than or equal to 3.5; a non-productive shale reservoir, a reservoir classification index (CCFJ) > 3.5;
Outputting a fine evaluation result diagram of a shale interval reservoir to be evaluated according to user requirements, wherein the fine evaluation result diagram comprises a horizon, a natural Gamma (GR), a drilling time (ZS), a depth, a lithology, a total hydrocarbon (Ct), methane (JW), a total organic carbon content (TOC), a Porosity (POR), a Brittleness Index (BI), a reservoir grading index (CCFJ), a preliminary explanation, fine evaluation and other diagrams, and characterizing explanation parameters and an evaluation result; when the chart is drawn, the same reservoir is graded into an interval;
the reservoir fine evaluation result chart characterizes the characteristics of reservoir interpretation parameters and the matching relation among the parameters: the natural gamma and lithology image reflects lithology change, the image reflects drillability during drilling, the total hydrocarbon and methane image reflects reservoir gas content, the total organic carbon image reflects hydrocarbon generation strength and hydrocarbon generation amount of reservoir hydrocarbon source rock, the porosity image reflects reservoir physical property, and the brittleness index image reflects reservoir compressibility; the preliminary explanation map, the reservoir grading index map and the fine evaluation map reflect the preliminary explanation and the fine evaluation results of the invention.
Example two
In this embodiment, the evaluation of the scheme is explained by taking a basin FL gas field marine shale reservoir a well as an application scenario.
(1) Application of the invention in Szechuan basin FL gas field sea-phase shale reservoir stratum A well
Drilling a shale layer section with a thickness of 152.0m and a well section of 3358.0-3510.0 m in a Longmaxi-Orotao system upper quincunx group under the well logging system, and determining the shale layer section to be evaluated according to the change of lithology, thickness, total hydrocarbon (Ct), total organic carbon content (TOC) and other interpretation parameters; according to regional interpretation standards, a layer-by-layer interpretation is carried out by taking meters as units, and a 20.0m/1 layer of a type I shale reservoir stratum, a 51.0m/1 layer of a type II shale reservoir stratum and an 81.0m/1 layer of a type III shale reservoir stratum are preliminarily interpreted;
carrying out multi-parameter linear regression by using the standardized preliminary interpretation node and the corresponding depth of total hydrocarbons (Ct), total organic carbon content (TOC), porosity (POR) and Brittleness Index (BI), wherein the correlation coefficient R =0.89 between the interpretation parameter and the preliminary conclusion shows that the parameter selection is effective, and the interpretation parameter influence coefficient A is obtained ct ＝-0.01、A TOC ＝-1.11、A POR ＝0.69、A BI = 0.01, correction constant B =3.51, fine-grained (fine-scale) index model CCFJ = -0.01 × Ct-1.11 × TOC +0.69 × POR-0.01 × BI +3.51, according to reservoir classification index model CCFJ = -0.01 × Ct-1.11 × TOC =Evaluating 14.0m/1 layer of the I type shale reservoir stratum, 53.0m/4 layer of the II type shale reservoir stratum and 85.0m/3 layer of the III type shale reservoir stratum (see figure 2);
the fine evaluation result indicates that the optimal selection of the horizontal well target frame is performed, the optimal well section of the sidetracking horizontal well is 3494.0-3508.0 m and is used as the target frame, the actual drilling horizontal section is 1680.0m, and the maximum stable gas production rate is 32.68 multiplied by 10 after the large-scale fracturing test of well completion 4 m 3 And/d, the gas test conclusion proves that the fine evaluation conclusion is reliable.
EXAMPLE III
In the embodiment, the evaluation of the scheme is explained by taking the FL gas field continental facies shale reservoir B well of the sichuan basin as an application scene.
(2) The invention is applied to the reservoir B well of FL gas field continental facies shale in the Sichuan basin
Drilling a shale layer in the Dongyue temple section of the gravity flow well group in the well Ju; according to regional interpretation standards, a layer-by-layer interpretation is carried out by taking meters as units, and a 1.0m/1 layer of a type I shale reservoir stratum, a 21.0m/4 layer of a type II shale reservoir stratum and a 36.0m/4 layer of a type III shale reservoir stratum are preliminarily interpreted;
carrying out multi-parameter linear regression by using the standardized preliminary interpretation node and the total hydrocarbon (Ct), total organic carbon content (TOC), porosity (POR) and Brittleness Index (BI) of the corresponding depth, wherein the correlation coefficient R =0.88 between the interpretation parameter and the preliminary conclusion shows that the interpretation parameter is effectively selected, and the influence coefficient A of the interpretation parameter is obtained Ct ＝-0.17、A TOC ＝-0.97、A POR ＝-0.02、A BI =0.006, correction constant B =3.70, fine evaluation of 5.0m/3, 19.0m/7, and 34.0m/5 of the type i shale reservoir, according to the reservoir classification index model CCFJ = -0.17 xct-0.97 xcto TOC-0.02 xcore +0.006 × BI +3.70 (see fig. 3); the fine evaluation result indicates that the optimal target frame of the horizontal well is drilled, the optimal well section 2437.0-2442.0 m of the sidetracking horizontal well is used as the target frame, the actual drilling horizontal section 1530.0m of the horizontal well is used as the actual drilling horizontal section, and the maximum stable gas production rate is 2.21 multiplied by 10 after the large fracturing test of well completion 4 m 3 D, oil production 18.0m 3 D, conclusion of test gasAnd the fine evaluation conclusion is reliable.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included in the present application.
Claims (10)
1. A unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis is characterized by comprising the following steps: comprises that
Acquiring logging information of a well to be evaluated, and determining a shale interval to be evaluated;
reading the interpretation parameters of the shale layer section of the well to be evaluated, and preliminarily interpreting the reservoir layer according to the work area interpretation standard;
selecting an explanation parameter and a preliminary explanation conclusion, and establishing a reservoir grading index model;
judging the correlation between the explanation parameters and the preliminary explanation conclusion;
calculating a reservoir grading index CCFJ by using a reservoir grading index model, and determining the reservoir category according to the size of the reservoir grading index CCFJ;
and outputting a fine evaluation result diagram of the shale interval to be evaluated.
2. The unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis according to claim 1, wherein the obtaining of logging information of the well to be evaluated and the determining of the shale interval to be evaluated comprise:
acquiring logging and logging information of a well to be evaluated, wherein the logging and logging information comprises a horizon, a well section, lithology, a drilling time ZS, a natural gamma GR, a total hydrocarbon Ct, methane JW, total Organic Carbon (TOC), porosity POR, gas content Q and brittleness index BI;
and determining the shale interval to be evaluated according to the lithology, the thickness H, the Ct of the total hydrocarbon, the TOC of the total organic carbon content and the POR interpretation parameter change of the well to be evaluated.
3. The method for finely evaluating unconventional oil and gas reservoirs based on logging multiparameter comprehensive analysis according to claim 2, wherein the determining the shale interval to be evaluated comprises:
selecting a shale layer section meeting the following conditions as a shale layer section to be evaluated;
the conditions comprise that the lithology is shale, the continuous effective thickness is not less than 15.0m, the total hydrocarbon Ct abnormity display is carried out, the hydrocarbon ratio KC is not less than 2, the total organic carbon content TOC of the sea-phase shale reservoir is not less than 1.0%, the total organic carbon content TOC of the continental-phase shale reservoir is not less than 0.5%, and the hydrocarbon ratio KC is the ratio of the total hydrocarbon Ct abnormity value to the base value.
4. The method for finely evaluating the unconventional oil and gas reservoir based on logging multi-parameter comprehensive analysis according to claim 1, wherein the reading of the interpretation parameters of the shale interval of the well to be evaluated and the preliminary interpretation of the reservoir according to the work area interpretation criteria comprises:
reading interpretation parameters including a total hydrocarbon Ct, a hydrocarbon ratio KC, a drilling time ratio ROPn/s, total organic carbon content TOC, porosity POR, gas saturation Sg and a brittleness index BI according to a depth corresponding relation;
aiming at different regions and different exploration strata, according to different reservoir types of marine facies and continental facies, adopting corresponding logging interpretation evaluation standards, interpreting shale layer sections layer by taking step length as a unit according to depth correspondence, and interpreting conclusions to be I-class shale reservoir, II-class shale reservoir and III-class shale reservoir.
5. The unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis according to claim 4, characterized in that: when the interpretation parameter combinations are mutually contradictory, four interpretation parameters of total organic carbon content TOC, brittleness index BI, porosity POR and total hydrocarbon Ct are selected according to tested gas well data in the area, and the interpretation results are given after all the parameters are compared according to the optimal sequence of the total organic carbon content TOC > brittleness index BI > porosity POR > total hydrocarbon Ct.
6. The method of claim 1, wherein the selecting interpretation parameters and preliminary interpretation conclusions and establishing a reservoir graded index model comprises:
selecting 3-4 interpretation parameters from the total hydrocarbon Ct, the total organic carbon content TOC, the porosity POR and the brittleness index BI according to logging information and regional characteristics, and reading a preliminary interpretation conclusion of the depth corresponding to the interpretation parameters;
carrying out standardization processing on the preliminary interpretation conclusion of the reservoir, so that the reservoir of the shale of class I =1, the reservoir of the shale of class II =2 and the reservoir of the shale of class III =3;
solving an influence coefficient and a correction constant of an interpretation parameter by utilizing multi-parameter linear regression analysis;
performing multiple linear regression analysis by using the initial interpretation conclusion after the standardization treatment and the interpretation parameters of the corresponding depth to obtain an interpretation parameter influence coefficient and a correction constant B;
and establishing a reservoir grading index model according to the interpretation parameter influence coefficients and the correction constant B, wherein the reservoir grading index CCFJ of the reservoir grading index model is obtained by adding the sum of the products of each interpretation parameter and the corresponding interpretation parameter influence coefficient and the correction constant B.
7. The method of claim 1, wherein the correlation of the interpretation parameters with the preliminary interpretation conclusion comprises:
if the multi-parameter linear regression correlation coefficient R is more than or equal to 0.7, the characterization interpretation conclusion and the selected interpretation parameter are in positive correlation relationship, and the next step is carried out;
if the multi-parameter linear regression correlation coefficient R is less than 0.7, the correlation between the characterization interpretation conclusion and the interpretation parameter is weak, the previous step is returned, and the interpretation parameter and the preliminary interpretation conclusion are optimized again for regression analysis.
8. The method for finely evaluating unconventional oil and gas reservoirs based on logging multiparameter comprehensive analysis according to claim 1, wherein the calculating a reservoir grading index CCFJ by using a reservoir grading index model, and the determining the reservoir category according to the size of the reservoir grading index CCFJ comprises:
substituting the interpretation parameters including the total hydrocarbon Ct, the total organic carbon content TOC, the porosity POR and the brittleness index BI into the reservoir grading index model, and calculating the reservoir grading index CCFJ of the corresponding depth;
and determining the reservoir categories according to the size of the reservoir grading index CCFJ, wherein the categories comprise a category I shale reservoir, a category II shale reservoir, a category III shale reservoir and a non-effective shale reservoir.
9. The method of claim 8, wherein the step of determining the reservoir classification according to the size of the reservoir classification index CCFJ comprises:
when the reservoir classification index CCFJ is less than or equal to 1.5, the reservoir is a shale reservoir of type I;
when the reservoir classification index CCFJ is more than 1.5 and less than or equal to 2.5, the reservoir is a type II shale reservoir;
when the reservoir classification index CCFJ is more than 2.5 and less than or equal to 3.5, the reservoir is a III-class shale reservoir;
and when the reservoir grading index CCFJ is more than 3.5, the reservoir is a non-effective shale reservoir.
10. The unconventional oil and gas reservoir fine evaluation method based on logging multi-parameter comprehensive analysis according to claim 1, wherein the outputting of the shale interval fine evaluation result graph to be evaluated comprises:
outputting a fine evaluation result diagram of the shale interval reservoir to be evaluated according to user requirements, wherein the fine evaluation result diagram comprises a horizon, a natural gamma GR, a ZS (zero-crossing-over) at the drilling time, a depth, a lithology, a total hydrocarbon Ct, a methane JW, a total organic carbon content TOC, a porosity POR, a brittleness index BI, a reservoir grading index CCFJ, a preliminary explanation, a fine evaluation and other diagrams, and characterizing reservoir explanation parameters and an evaluation result.
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