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CN112196513A - Productivity prediction method for shale gas wells in Longmaxi Formation based on horizontal well trajectory evaluation - Google Patents

Productivity prediction method for shale gas wells in Longmaxi Formation based on horizontal well trajectory evaluation Download PDF

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CN112196513A
CN112196513A CN202011048618.7A CN202011048618A CN112196513A CN 112196513 A CN112196513 A CN 112196513A CN 202011048618 A CN202011048618 A CN 202011048618A CN 112196513 A CN112196513 A CN 112196513A
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CN112196513B (en
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葛祥
张正玉
颜磊
黎泽刚
吴见萌
董震
缪祥禧
何传亮
林绍文
韩芳芳
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Southwest Measurement And Control Co Of Sinopec Jingwei Co ltd
China Petrochemical Corp
Sinopec Oilfield Service Corp
Sinopec Jingwei Co Ltd
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Sinopec Oilfield Service Corp
Sinopec Southwest Petroleum Engineering Co Ltd Logging Branch
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Abstract

本发明公开了基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，1，收集区域上测试井资料；2，进行分段优选；3，计算水平井各段的页岩气层参数；4，获取已试井页岩气层压裂参数；5，获取无阻流量AOFg；6，计算已试井水平段各段的含气指数Ig、压裂改造指数IFR、及CPIg；7，确定模型AOFg/MGOg＝A×eB×CPIg的模型系数A、B；8，对待预测井进行分段优选；9，对待预测井各段页岩气层参数进行计算统计；10，获取待预测井页岩气层压裂参数；11，计算Igi、IFRi及CPIgi；12，计算待预测井页岩气层绝对无阻流量。本发明结合其水平段在小层中的穿行轨迹和储层品质，采用基于轨迹建立的页岩气产能预测计算模型，在实际计算中具有较高的精度，应用效果较好。

Figure 202011048618

The invention discloses a productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation. 1. Collecting test well data in the region; 2. Performing segment optimization; 3. Calculating shale gas layer parameters of each segment of the horizontal well; 4. Obtain the fracturing parameters of the tested shale gas layer; 5. Obtain the open flow rate AOFg; 6. Calculate the gas-bearing index Ig, the fracturing stimulation index IFR, and CPIg of each horizontal section of the well-tested well; 7. Determine the model AOFg/MGOg=A×e B×CPIg model coefficients A and B; 8. Perform segmental optimization of wells to be predicted; 9. Calculate and count the parameters of shale gas layers in each section of wells to be predicted; 10. Obtain well sheets to be predicted rock gas layer fracturing parameters; 11, calculate Igi, IFRi and CPIgi; 12, calculate the absolute unobstructed flow rate of the shale gas layer in the well to be predicted. The present invention adopts the shale gas production capacity prediction calculation model established based on the trajectory in combination with the traveling trajectory of the horizontal section in the small layer and the quality of the reservoir, and has high precision in actual calculation and good application effect.

Figure 202011048618

Description

Longmaxi group shale gas well productivity prediction method based on horizontal well trajectory evaluation
Technical Field
The invention relates to the technical field of shale gas logging evaluation, and mainly relates to a Longmaxi shale gas well productivity prediction method based on horizontal well trajectory evaluation.
Background
Unlike conventional reservoir gas reservoirs, shale is both a source rock for natural gas generation and a reservoir and cap rock for gathering and storing natural gas. Shale gas reservoirs have the characteristics of ultralow porosity and permeability, and effective exploitation with certain economic value can be obtained only by drilling a horizontal well and modifying the shale gas reservoir through large-scale hydraulic fracturing. Due to the particularity of development, the capacity prediction of the shale gas reservoir is more complex than that of the conventional gas reservoir. Taking the kamung shale gas field in the south of the Sichuan province as an example, the burial depth of the shale gas target layer in the Longmaxi group-Wufeng group is about 3500-.
Through literature research, the shale gas horizontal well productivity can be predicted by using well drilling and completion data, well logging data and fracturing construction parameters. The productivity prediction model mainly takes three productivity analysis methods, namely an empirical method, an analytical method and a numerical simulation method, as entry points to predict the shale gas productivity. The empirical method is mainly based on the exploitation practice of shale gas reservoirs, the actual output data of shale gas exploitation is subjected to fitting comparison and analysis, and key factors influencing the shale gas productivity are found out; the analytical method is based on two aspects of establishing a shale gas reservoir productivity theoretical model and deducing a calculation formula; the numerical simulation method is a key research on the shale gas horizontal well seepage adsorption mechanism before the simulation of fracturing. Due to the regional characteristics and differences of different areas of the shale gas reservoir, the model has no universality in actual production application. The specific condition of shale gas development in the south Sichuan work area is combined, and because the horizontal well test results are few and the well test data is incomplete, the method is limited by theory and practice no matter the method is adopted. The horizontal well productivity prediction method based on fine track evaluation is provided, the existing test data are combined, and the geological factors are considered, wherein the method comprises the following steps: organic carbon, porosity, gas saturation, equivalent segment length, thickness of a high-quality reservoir of the pilot hole, brittleness index and the like; engineering factors include: total fracturing fluid amount, total fracturing sand volume, total perforation cluster number, single-segment perforation cluster number and other engineering parameters.
The shale gas reservoir of the Kainan quinquet-Longmaxi group has 9 geological small layers (layer No. + -) from top to bottom, wherein the high-quality reservoir is mainly concentrated from layer No. + -. small layer to layer No. + -. small layer, and the layer I-III in the layer No. + -. small layer is taken as the most favorable high-quality shale reservoir layer section, so that the shale gas reservoir is a remarkable reference mark for evaluating the track of the horizontal well.
Comparative analysis (table 1) on test results of shale gas reservoir horizontal wells in south china indicates that: the shale gas reservoir horizontal well productivity has more influencing factors, the productivity is related to gas reservoir geological characteristic parameters such as reservoir burial depth, high-quality reservoir thickness, reservoir quality parameters (lithology, physical property, organic carbon content, gas content and the like), horizontal segment reservoir drilling rate, formation pressure and the like, is also influenced by engineering quality characteristic parameters such as horizontal well segment length, formation fracturing capability and the like, and is also related to reservoir modification fracturing parameters and horizontal well tracks.
TABLE 1 comparison table of testing conditions of deep shale gas horizontal well in south of Sichuan
Figure BDA0002708820140000011
Figure BDA0002708820140000021
Currently, shale gas wells in the south of the Sichuan are still under development, horizontal wells have fewer test results, and well testing data are incomplete, so that the productivity is mainly considered and predicted and evaluated by the following three methods in the productivity model research: the method comprises the steps of firstly, generating capacity prediction model based on the effective length of a horizontal well section; secondly, a productivity prediction model for explaining the type of the gas layer based on horizontal well logging; and thirdly, a comprehensive index grading evaluation model based on segmentation optimization. The first two, although operable, are relatively less accurate (fig. 1, 2); the third method is carried out on the basis of analysis of a horizontal well traversing track, and the model precision is higher than that of the former two methods, so that the method can be used as a recommendation method for quantitative evaluation of shale gas productivity in a local area.
Disclosure of Invention
The invention establishes the quantitative yield prediction method of the shale gas horizontal well by considering the geological and engineering dual-dessert quality factors and also considering the fracturing scale and the track space position characteristics.
The purpose of the invention is realized by the following technical scheme:
the method for predicting the productivity of the shale gas well in the Longmaxi group based on the evaluation of the track of the horizontal well comprises the following steps:
step 1, collecting data of a test well on an area;
step 2, performing development analysis on the drilling trajectory of the tested horizontal well according to the logging information, and performing segmented optimization;
step 3, calculating shale gas layer parameters of each section of the horizontal well through logging information;
step 4, acquiring fracturing parameters of the tested shale gas layer;
step 5, obtaining the non-resistance flow AOFg of the tested well shale gas layer through the productivity test data;
step 6, sequentially calculating the gas containing index Ig, the fracturing modification index IFR and the comprehensive gas production index CPIg of each section of the tested well horizontal section by using the parameters obtained in the steps 3 and 4;
step 7, determining a model AOFg/MGOg (the Axe) by using a least square method according to the obtained non-resistance flow rate of the tested shale gas layer/the maximum daily gas production rate MGOg and the corresponding comprehensive gas production index CPIg calculated by the tested wellB×CPIgA, B;
step 8, according to the typical test well anatomy situation, performing segmented optimization on the well to be predicted, wherein the optimization principle is as follows: the horizontal well track does not cross geological small layers, faults and cracks to relatively develop; the fracturing fluid has the advantages of capability of fracturing, high gas content and good well cementation quality;
step 9, repeating the step 3 to calculate and count the shale gas layer parameters of each section of the well to be predicted;
step 10, repeating the step 4 to obtain fracturing parameters of the gas reservoir of the well shale to be predicted, wherein the specific parameters are fracturing fluid volume Vf and sand volume Vs;
step 11, sequentially calculating a gas containing index Igi, a fracturing modification index IFRi and a comprehensive gas production index CPIGi of each section of the gas layer to be predicted by using the parameters obtained in the step 9 and the step 10;
step 12, using model AOFg ═ A × eB×CPIgAnd calculating the absolute unobstructed flow of the well shale gas layer to be predicted.
Preferably, the test well data includes geological engineering parameter data and test productivity.
Preferably, the position of each segment of the preferred segment is consistent with the actual test capacity plan.
Preferably, the length L of the segmented segment is controlled to within + -30 cm from the number Cn of perforation clusters in the segment multiplied by the cluster spacing r.
As a preferred mode, the shale gas layer parameters comprise the thickness H of a straight pilot hole high-quality reservoir, the length L of a horizontal section reservoir, a pressure coefficient Kf, porosity POR, gas saturation Sg, organic carbon content TOC and brittleness index Brit; a premium reservoir is defined herein as having a TOC > 2%.
Preferably, the fracturing parameters of the tested shale gas layer comprise the volume Vf (m) of the fracturing fluid3) And frac sand volume Vs (m)3)。
As a preferred mode of execution,
calculating shale gas layer parameters of each section of the horizontal well through logging data: the method comprises the following steps of (1) directly guiding the thickness H of a high-quality reservoir, the length L of a horizontal section reservoir, a pressure coefficient Kf, porosity POR, gas saturation Sg, organic carbon content TOC and brittleness index Brit;
obtaining fracturing parameters of a tested well shale gas layer: volume Vf (m) of fracturing fluid3) And frac sand volume Vs (m)3)；
Obtaining the non-resistance flow AOFg of the tested well shale gas layer through the productivity test data;
sequentially calculating a gas-containing index Ig, a fracturing modification index IFR and a comprehensive gas production index CPIg of each section of the tested horizontal section by using the shale gas layer parameters of each section of the horizontal well and the parameters obtained by the fracturing parameters of the tested shale gas layer; the specific formula is as follows:
gas index Ig:
Igi＝L1×H×Kfi×PORi×Sgi×TOCi
in the above formula, IgiIs the gas index of the i section, and is dimensionless; l isiIs the horizontal segment length of the ith segment in m; h is the thickness of a high-quality reservoir (gas layer, TOC > 2%) of a straight pilot hole, and the unit is m; kfiIs the reservoir pressure coefficient of the ith section without dimension; POR (Portable Audio Port) deviceiIs the average porosity of the reservoir in section i in units%; sgiThe average gas saturation of the reservoir in the ith section is unit percent; TOCiIs the average organic carbon content in the reservoir of the ith section;
fracture reformation index FI:
FIi＝a×(Vf+Vs)×Cni/Cn
in the above formula, a is a formula empirical coefficient and is dimensionless; FIiThe fracture transformation index of the ith section is dimensionless; vf is the total fracturing fluid volume of the whole horizontal section, unit square (m)3) (ii) a Vs is the total frac sand volume in units (m) for the entire horizontal section3)；CniThe number of perforating clusters contained in the ith section is dimensionless; cn is the total perforation cluster number of the whole horizontal section and is dimensionless;
comprehensive gas production index CPIg:
comprehensive gas production index CPIg of each section of reservoiri：
CPIgi＝(Igi+FIi)×Briti
In the above formula, BritiThe average brittleness index of the reservoir in the ith section is unit percent;
comprehensive gas production index CPIg corrected by small layer influencei′：
CPIg′i＝[Igi×(1+αi×a1)+FIi×(1+αi×a2)]×Briti
In the above formula, αiThe correction term of the influence factor of the geological small layer where the ith section of reservoir is located is dimensionless. a is1、a2Influencing factor alpha for traversing small layersiThe influence weight distribution coefficients of the gas index and the fracturing index are respectively, the default empirical values are a 1-0.003 and a 2-0.007), and the method is dimensionless;
wherein the factor of influence of the small layer is traversediThe calculation is as follows:
αi＝H/si
in the above formula, H is the thickness of the premium reservoir, defined herein as TOC>2%, unit m; siThe distance from the top boundary or the bottom boundary position of a geological small layer in a straight pilot hole well corresponding to the ith section of reservoir horizontally running to the middle position of a high-quality reservoir of the straight pilot hole is measured in meters;
and finally, the total comprehensive gas production index CPIg of the whole section is the sum of the gas production indexes of all sections:
Figure BDA0002708820140000041
in the above formula, the CPIg is the comprehensive gas production index of the whole horizontal section, and is dimensionless; n is the preferred total number of segments for the horizontal segment segmentation.
Preferably, a is 0.002.
Preferably, a1＝0.003，a2＝0.007。
Preferably, siS < 0.5mi＝0.5m。
The invention has the beneficial effects that:
the method combines the traversing track of the horizontal section in the small layer and the quality of the reservoir layer, adopts the shale gas capacity prediction calculation model established based on the track, and has higher precision and better application effect in actual calculation.
Drawings
FIG. 1 is a graph of the relationship between the test unimpeded flow and the effective segment length implemented by the present invention;
FIG. 2 is a graph of unobstructed flow versus type I gas zone length for the practice of the present invention;
FIG. 3 is a graph of horizontal segment capacity versus segment model for the practice of the present invention;
FIG. 4 is a graph of unobstructed flow versus synthetic index for a patent application embodying the present invention;
FIG. 5 is a graph showing the relationship between the model prediction result and the test non-resistance flow;
FIG. 6 is a flow chart of a method embodying the present invention;
FIG. 7 is a statistical table of synthetic index model parameters after an example xXhorizontal well log segment optimization.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
The implementation flow of the method for predicting the productivity of the shale gas well in the Longmaxi group based on the evaluation of the track of the horizontal well is shown in FIG. 6, and the method comprises the following steps:
step 1, collecting data of a test well on an area;
step 2, performing development analysis on the drilling trajectory of the tested horizontal well according to the logging information, and performing segmented optimization;
step 3, calculating shale gas layer parameters of each section of the horizontal well through logging information;
step 4, acquiring fracturing parameters of the tested shale gas layer;
step 5, obtaining the non-resistance flow AOF of the tested well shale gas layer through the productivity test datag；
Step 6, sequentially calculating the gas containing index Ig, the fracturing modification index IFR and the comprehensive gas production index CPIg of each section of the tested well horizontal section by using the parameters obtained in the steps 3 and 4;
step 7, determining a model AOFg/MGOg (the Axe) by using a least square method according to the obtained non-resistance flow rate of the tested shale gas layer/the maximum daily gas production rate MGOg and the corresponding comprehensive gas production index CPIg calculated by the tested wellB×CPIgModel coefficients A, B (fig. 4);
step 8, according to the typical test well anatomy situation, performing segmented optimization on the well to be predicted, wherein the optimization principle is as follows: the horizontal well track does not cross geological small layers, faults and cracks to relatively develop; the fracturing fluid has the advantages of capability of fracturing, high gas content and good well cementation quality;
step 9, repeating the step 3 to calculate and count the shale gas layer parameters of each section of the well to be predicted;
step 10, repeating the step 4 to obtain fracturing parameters of the shale gas layer to be predicted;
step 11, sequentially calculating a gas containing index Igi, a fracturing modification index IFRi and a comprehensive gas production index CPIGi of each section of the gas layer to be predicted by using the parameters obtained in the step 9 and the step 10;
step 12, using model AOFg ═ A × eB×CPIgAnd calculating the absolute unobstructed flow of the well shale gas layer to be predicted.
In a preferred embodiment, the test well data includes geological engineering parameter data and test productivity.
In a preferred embodiment, the segment positions of the preferred segments are consistent with the actual test capacity plan.
In a preferred embodiment, the length L of the segmented segment is equal to the number Cn of clusters in the segment multiplied by the cluster spacing r or L equals the number Cn of clusters multiplied by the cluster spacing r + -30 cm)
In a preferred embodiment, the shale gas formation parameters include a straight-hole premium reservoir (gas formation thickness, TOC > 2%) thickness H, a horizontal interval reservoir length L, a pressure coefficient Kf, a porosity POR, a gas saturation Sg, an organic carbon content TOC, and a brittleness index Brit.
In a preferred embodiment, the fracturing parameters of the tested well shale gas formation comprise the volume Vf (m) of the fracturing fluid3) And frac sand volume Vs (m)3)。
In a preferred embodiment, shale gas layer parameters of each section of the horizontal well are calculated through logging data: the method comprises the following steps of (1) directly guiding the thickness H of a high-quality reservoir (the thickness of a gas layer, wherein the TOC is more than 2%), the length L of a horizontal section reservoir, a pressure coefficient Kf, porosity POR, gas saturation Sg, organic carbon content TOC and brittleness index Brit;
obtaining fracturing parameters of a tested well shale gas layer: volume Vf (m) of fracturing fluid3) And frac sand volume Vs (m)3)；
Obtaining the non-resistance flow AOFg of the tested well shale gas layer through the productivity test data;
sequentially calculating a gas-containing index Ig, a fracturing modification index IFR and a comprehensive gas production index CPIg of each section of the tested horizontal section by using the shale gas layer parameters of each section of the horizontal well and the parameters obtained by the fracturing parameters of the tested shale gas layer; the specific formula is as follows:
gas index Ig:
Igi＝Li×H×Kfi×PORi×Sgi×TOCi
in the above formula, IgiIs the gas index of the i section, and is dimensionless; l isiIs the horizontal segment length of the ith segment in m; h is a direct pilot hole high-quality reservoir (gas layer, TOC)>2%) thickness (or gas reservoir height), in m; kfiIs the reservoir pressure coefficient of the ith section without dimension; POR (Portable Audio Port) deviceiIs the average porosity of the reservoir in section i in units%; sgiThe average gas saturation of the reservoir in the ith section is unit percent; TOCiIs the average organic carbon content in the reservoir of the ith section;
fracture reformation index FI:
FIi＝a×(Vf+Vs)×Cni/Cn
in the above formula, a is formula experienceCoefficient, dimensionless; FIiThe fracture transformation index of the ith section is dimensionless; vf is the total fracturing fluid volume of the whole horizontal section, unit square (m)3) (ii) a Vs is the total frac sand volume in units (m) for the entire horizontal section3)；CniThe number of perforating clusters contained in the ith section is dimensionless; cn is the total perforation cluster number of the whole horizontal section and is dimensionless;
comprehensive gas production index CPIg:
comprehensive gas production index CPIg of each section of reservoiri：
CPIgi＝(Igi+FIi)×Briti
In the above formula, BritiThe average brittleness index of the reservoir in the ith section is unit percent;
comprehensive gas production index CPIg corrected by small layer influencei′：
CPIg′i＝[Igi×(1+αi×a1)+FIi×(1+αi×a2)]×Briti
In the above formula, αiThe method is a correction term of influence factors of a geological small layer (a first layer to a third layer or peaks I, II and III) where the ith section of reservoir is located, and is dimensionless. a is1、a2Influencing factor alpha for traversing small layersiWeight distribution coefficients of influence on gas index and fracturing index, respectively (which can be set as a in this study)1＝0.003，a20.007), dimensionless;
wherein the factor of influence of the small layer is traversediThe calculation is as follows:
αi＝H/si
in the above formula, H is a direct-leading-hole high-quality reservoir (gas layer, TOC)>2%) thickness (or gas reservoir height), in m; siThe distance from the top boundary or bottom boundary position (corresponding to the peak I, the peak II and the peak III when the peak is taken) of the geological small layer in the straight pilot hole well corresponding to the i-th section of reservoir horizontally passes to the middle position of the peak II and the peak III in the straight pilot hole is measured in meters;
and finally, the total comprehensive gas production index CPIg of the whole section is the sum of the gas production indexes of all sections:
Figure BDA0002708820140000061
in the above formula, the CPIg is the comprehensive gas production index of the whole horizontal section, and is dimensionless; n is the preferred total number of segments for the horizontal segment segmentation.
In a preferred embodiment, a is 0.002.
In a preferred embodiment, a1＝0.003，a2＝0.007。
In a preferred embodiment, siS < 0.5mi＝0.5m。
Taking an x horizontal well as an example, combining the traversing track of the horizontal section in the small layer and the quality of the reservoir, firstly, preferentially dividing the reservoir in the gas testing section into 6 sections: 4125-4200 m, 4200-4340.5 m, 4340.5-4845 m, 4845-5020 m, 5020-5109.3 m and 5109.3-5293 m; secondly, acquiring total fracturing parameters (fracturing fluid volume Vf and sand volume) of the well, the total perforating cluster number Cn, the thickness H (or gas reservoir height) of a high-quality reservoir (gas layer, TOC > 2%), the distance s between each small layer (or peak I and peak II) of the straight pilot hole and a peak III, and the horizontal section stratum pressure coefficient (reservoir middle depth); finally, respectively counting the length L of each horizontal well section of the shale gas layer of 6 sections, the porosity POR, the gas saturation Sg, the organic carbon content TOC, the number Cn of perforating clusters and a small layer passing influence factor alpha; the gas index Ig, the fracture reformation index IFR and the final capacity integration index CPIg for the 6-stage gas formation are calculated, see the table in fig. 7.
Calculating the capacity by using geological engineering data for a plurality of shale gas horizontal wells in the Sichuan basin by adopting a shale gas capacity prediction calculation model established based on the track; from the calculation result, the prediction method has higher precision and better application effect. The calculated result has good correlation with the actual test well, and the correlation coefficient R20.88 as in fig. 5.
From the perspective of gas reservoir fracturing, the final capacity of the shale gas horizontal well can be regarded as the sum of the capacities contributed by the small test gas sections. From the evaluation of the travel track and the geological quality, the gas content (index) of different travel sections is different, and the final productivity of the section is changed. Therefore, the segmentation can be performed according to a certain fracturing and quality evaluation principle, and after the productivity indexes of all the small segments are obtained, the sum of the productivity indexes is used as the total productivity (see fig. 3).
The biggest advantage of the piecewise model in finding the product is that in addition to characterizing the 'double-quality' factors (geological quality and engineering quality) of each segment, the spatial variation of the trajectory can be characterized by using the segmentation, and the main method adopts three spatial factors of horizontal segment length L, gas height H and relative distance s for expression: wherein horizontal segment length refers to the length of each segment; gas bearing height refers to the vertical height of a fracturable gas reservoir (generally expressed as the thickness of a straight pilot premium reservoir); the relative distance refers to the distance from the center of each section to the gas reservoir geologic small layer model relative to a fixed reference point, and is generally represented by the distance from the position of the top boundary (or bottom boundary) of the geologic small layer corresponding to the section in the straight pilot hole well to the position of a reference depth point in the straight pilot hole (for example, the position of the top of a No. III peak in the straight pilot hole) (see FIG. 3).
From three space factors, the horizontal section length L and the gas-bearing height H are closely related to the gas-bearing volume of the section of reservoir, and the relative distance s changes along with the change of drilling exposure small layers, so that the relative change position of each section of track can be expressed. As can be known from the previous research, the drilling trajectory of the horizontal well is a sensitive factor influencing the productivity, so that the performance can be represented by using the three space factors to obtain a better effect.
According to the method, the existing test well data is used as a basis, geological engineering factors related in the shale gas volume transformation process are calculated, the relative position of a track in a transformation volume space is depicted, the output energy of the horizontal well can be accurately obtained, the problem of less test well data is solved, and the theoretical method under the complex boundary condition is avoided. The key point of the invention is the shale gas horizontal well productivity prediction method based on the well track, which solves the problem of less data of a test well, avoids adopting a theoretical method under a complex boundary condition and has higher precision. The protection point of the method is a shale gas horizontal well productivity prediction method based on a fine borehole trajectory. The comprehensive productivity prediction model based on the horizontal well traversing track relationship, which is established by the invention, solves the problem of less data of the test well, and avoids the adoption of a theoretical method under a complex boundary condition, so that the proposed method has the advancement.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, it should be noted that any modifications, equivalents and improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1.基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于，包括如下步骤：1. the Longmaxi Formation shale gas well productivity prediction method based on the evaluation of horizontal well trajectory, is characterized in that, comprises the steps: 步骤1，收集区域上测试井资料；Step 1, collect test well data in the area; 步骤2，依据测井资料对已试井水平井钻遇轨迹展开分析，并进行分段优选；Step 2, according to the logging data, analyze the drilling trajectories of the tested horizontal wells, and perform segmentation optimization; 步骤3，通过测井资料计算水平井各段的页岩气层参数；Step 3: Calculate the shale gas layer parameters of each section of the horizontal well through the logging data; 步骤4，获取已试井页岩气层压裂参数；Step 4, obtaining the fracturing parameters of the well-tested shale gas layer; 步骤5，通过测试产能资料获取已试井页岩气层无阻流量AOFg；Step 5: Obtain the unobstructed flow rate AOFg of the tested shale gas layer through the test productivity data; 步骤6，运用步骤3和4获取的参数依次计算已试井水平段各段的含气指数Ig、压裂改造指数IFR、及整个水平段的综合产气指数CPIg；Step 6, using the parameters obtained in steps 3 and 4 to sequentially calculate the gas-bearing index Ig of each section of the horizontal section of the well tested, the fracturing stimulation index IFR, and the comprehensive gas production index CPIg of the entire horizontal section; 步骤7，将获取的已试井页岩气层无阻流量AOFg/最高日产气量MGOg与相对应已试井计算的综合产气指数CPIg利用最小二乘法确定模型AOFg/MGOg＝A×eB×CPIg的模型系数A、B；Step 7: Use the least squares method to determine the model AOFg/MGOg=A×e B×CPIg by using the obtained unobstructed flow rate AOFg/maximum daily gas production MGOg of the well-tested shale gas layer and the corresponding comprehensive gas production index CPIg calculated by the well-tested well The model coefficients A and B of ; 步骤8，根据典型测试井解剖情况，对待预测井进行分段优选；Step 8, according to the anatomy of typical test wells, perform segmental optimization of wells to be predicted; 步骤9，重复步骤3对待预测井各段页岩气层参数进行计算统计；Step 9, repeat Step 3 to calculate and count the shale gas layer parameters of each section of the well to be predicted; 步骤10，重复步骤4获取待预测井页岩气层压裂参数；Step 10, repeat step 4 to obtain the shale gas layer fracturing parameters of the well to be predicted; 步骤11，运用步骤9和步骤10中获取的参数依次计算待预测井气层各段的含气指数Igi、压裂改造指数IFRi及综合产气指数CPIgi；Step 11, using the parameters obtained in steps 9 and 10 to sequentially calculate the gas-bearing index Igi, the fracturing stimulation index IFRi and the comprehensive gas production index CPIgi of each section of the gas layer of the well to be predicted; 步骤12，利用模型AOFg＝A×eB×CPIg计算待预测井页岩气层绝对无阻流量。Step 12, using the model AOFg=A×e B×CPIg to calculate the absolute open flow rate of the shale gas layer in the well to be predicted. 2.根据权利要求1所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：测试井资料包括地质工程参数数据和测试产能。2 . The productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation according to claim 1 , wherein the test well data includes geological engineering parameter data and test productivity. 3 . 3.根据权利要求2所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：优选分段的各段位置与实际测试产能方案一致。3 . The productivity prediction method for Longmaxi Formation shale gas wells based on the evaluation of horizontal well trajectory according to claim 2 , wherein the positions of each segment of the optimal segment are consistent with the actual test productivity plan. 4 . 4.根据权利要求1-3任一所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：分段段长L与该段内的射孔簇数Cn×簇间距r得到的值控制在±30cm内。4. The method for predicting the productivity of Longmaxi Formation shale gas wells based on the evaluation of horizontal well trajectory according to any one of claims 1-3, characterized in that: the segment length L and the number of perforation clusters in the segment Cn×clusters The value obtained for the distance r is controlled within ±30cm. 5.根据权利要求1所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：页岩气层参数包括直导眼优质储层厚度H、水平段储层长度L、压力系数Kf、孔隙度POR、含气饱和度Sg、有机碳含量TOC以及脆性指数Brit。5. The productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation according to claim 1, wherein the shale gas layer parameters include the thickness H of the high-quality reservoir of the straight pilot hole and the length L of the horizontal section reservoir , pressure coefficient Kf, porosity POR, gas saturation Sg, organic carbon content TOC and brittleness index Brit. 6.根据权利要求1所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：已试井页岩气层压裂参数包括压裂液体积Vf(m3)和压裂砂体积Vs(m3)。6 . The productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation according to claim 1 , wherein: the well-tested shale gas layer fracturing parameters include fracturing fluid volume Vf (m 3 ) and Fracturing sand volume Vs (m 3 ). 7.根据权利要求1所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：通过测井资料计算水平井各段的页岩气层参数：直导眼优质储层厚度H、水平段储层长度L、压力系数Kf、孔隙度POR、含气饱和度Sg、有机碳含量TOC以及脆性指数Brit；7. The Longmaxi Formation shale gas well productivity prediction method based on the evaluation of horizontal well trajectory according to claim 1, is characterized in that: calculating the shale gas layer parameters of each section of the horizontal well by logging data: straight pilot hole high-quality reservoir Layer thickness H, horizontal reservoir length L, pressure coefficient Kf, porosity POR, gas saturation Sg, organic carbon content TOC and brittleness index Brit; 获取已试井页岩气层压裂参数：压裂液体积Vf(m3)和压裂砂体积Vs(m3)；Obtain the fracturing parameters of the tested shale gas layer: fracturing fluid volume Vf (m 3 ) and fracturing sand volume Vs (m 3 ); 通过测试产能资料获取已试井页岩气层无阻流量AOFg；Obtain the unobstructed flow AOFg of the tested shale gas layer through the test productivity data; 运用水平井各段的页岩气层参数和已试井页岩气层压裂参数获取的参数依次计算已试井水平段各段的含气指数Ig、压裂改造指数IFR、及整个水平段的综合产气指数CPIg；具体公式如下：Using the shale gas layer parameters of each section of the horizontal well and the parameters obtained from the shale gas layer fracturing parameters of the well-tested well, the gas-bearing index Ig, the fracturing stimulation index IFR of each section of the horizontal section of the well-tested well, and the entire horizontal section are calculated in turn. The comprehensive gas production index CPIg; the specific formula is as follows: 含气指数Ig：Gas index Ig: Ig1＝L1×H×Kf1×POR1×sg1×TOC1 Ig 1 =L 1 ×H×Kf 1 ×POR 1 ×sg 1 ×TOC 1 上式中，Igi为第i段的含气指数，无量纲；Li为第i段的水平段长，单位m；H为直导眼优质储层(气层，TOC＞2％)厚度(或气藏高度)，单位m；Kfi为第i段的储层压力系数，无量纲；PORi为第i段的储层平均孔隙度，单位％；Sgi为第i段的储层平均含气饱和度，单位％；TOCi为第i段的储层平均有机碳含量，单位％；In the above formula, Igi is the gas-bearing index of the i -th section, dimensionless; Li is the length of the horizontal section of the i -th section, in m; H is the thickness of the high-quality reservoir (gas layer, TOC>2%) of the straight pilot hole (or gas reservoir height), in m; Kfi is the reservoir pressure coefficient of the i -th stage, dimensionless; POR i is the average porosity of the i-th stage of the reservoir, in %; Sgi is the reservoir of the i -th stage Average gas saturation, unit %; TOC i is the average organic carbon content of the reservoir in the i-th stage, unit %; 压裂改造指数FI：Fracturing transformation index FI: FIi＝a×(Vf+Vg)×Cn1/CnFI i =a×(Vf+V g )×Cn 1 /Cn 上式中，a为公式经验系数，无量纲；FIi为第i段的压裂改造指数，无量纲；Vf为整个水平段的总压裂液体积，单位方(m3)；Vs为整个水平段的总压裂砂体积，单位(m3)；Cni为第i段所含的射孔簇数，无量纲；Cn为整个水平段的总射孔簇数，无量纲；In the above formula, a is the empirical coefficient of the formula, dimensionless; FI i is the fracturing stimulation index of the i-th section, dimensionless; Vf is the total fracturing fluid volume of the entire horizontal section, in unit square (m 3 ); Vs is the entire Total volume of fracturing sand in the horizontal section, unit (m 3 ); Cn i is the number of perforation clusters in the i-th section, dimensionless; Cn is the total number of perforation clusters in the entire horizontal section, dimensionless; 综合产气指数CPIg：Composite gas production index CPIg: 每段储层的综合产气指数CPIgi：The comprehensive gas production index CPIg i of each reservoir section: CPIgi＝(Igi+FIi)×Briti CPIg i =(Ig i +FI i )×Brit i 上式中，Briti为第i段的储层平均脆性指数，单位％；In the above formula, Brit i is the average brittleness index of the reservoir in the i-th section, in %; 经穿行小层影响校正后的综合产气指数CPIgi′：The comprehensive gas production index CPIg i ′ corrected for the effect of passing through the sublayer: CPIg′i＝[Igi×(1+αi×a1)+FIi×(1+α1×a2)]×Brit1 CPIg′ i =[Ig i ×(1+α i ×a 1 )+FI i ×(1+α 1 ×a 2 )]×Brit 1 上式中，αi为第i段储层所处地质小层的影响因子校正项，无量纲；a1、a2为穿行小层影响因子αi分别对含气指数和压裂指数的影响权重分配系数，无量纲；In the above formula, α i is the influence factor correction term of the geological sublayer where the i-th reservoir is located, which is dimensionless; a 1 and a 2 are the influences of the influence factor α i on the gas-bearing index and the fracturing index, respectively. Weight distribution coefficient, dimensionless; 其中，穿行小层影响因子αi计算如下：Among them, the impact factor α i of passing through the small layer is calculated as follows: αi＝H/si α i =H/s i 上式中，H为直导眼优质储层，优质储层定义为TOC＞2％，单位m；si为水平穿行的第i段储层所对应直导眼井中地质小层顶界或底界位置到直导眼中II号、III号峰中部位置的距离，单位为米；In the above formula, H is the high-quality reservoir of the straight pilot hole, and the high-quality reservoir is defined as TOC > 2%, in m; s i is the top boundary or bottom of the geological layer in the straight pilot hole corresponding to the i-th reservoir that runs horizontally. The distance from the boundary position to the middle position of No. II and No. III peaks in the direct guide eye, in meters; 最后全段总的综合产气指数CPIg为各段产气指数之和：Finally, the total comprehensive gas production index CPIg of the whole section is the sum of the gas production indices of each section:
Figure FDA0002708820130000021
Figure FDA0002708820130000021
 上式中，CPIg为整个水平段的产气综合指数，无量纲；n为水平段分段优选的总段数。In the above formula, CPIg is the comprehensive gas production index of the entire horizontal section, which is dimensionless; n is the total number of sections that are optimal for the horizontal section. 8.根据权利要求7所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：a＝0.002。8 . The productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation according to claim 7 , wherein a=0.002. 9 . 9.根据权利要求7所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：a1＝0.003，a2＝0.007。9 . The productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation according to claim 7 , wherein a 1 =0.003, a 2 =0.007. 10 . 10.根据权利要求7所述的基于水平井轨迹评价的龙马溪组页岩气井产能预测方法，其特征在于：si＜0.5m时，si＝0.5m。10 . The productivity prediction method for Longmaxi Formation shale gas wells based on horizontal well trajectory evaluation according to claim 7 , wherein: when s i <0.5 m, s i =0.5 m. 11 .
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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113673771A (en) * 2021-08-24 2021-11-19 中国石油化工股份有限公司 Shale gas horizontal well fracturing segmentation method
CN113935161A (en) * 2021-09-29 2022-01-14 西南石油大学 Method for designing economic length of horizontal section of geothermal heating U-shaped butt-joint well
CN114693086A (en) * 2022-03-15 2022-07-01 湖南继善高科技有限公司 A fracturing evaluation method, system and storage medium based on electromagnetic fracturing monitoring
CN115330060A (en) * 2022-08-17 2022-11-11 中国科学院大学 Calculation method of productivity of shale oil horizontal well based on analysis of reservoir and engineering factors
CN119047171A (en) * 2024-08-20 2024-11-29 四川越盛能源集团有限公司 Pre-drilling EUR prediction method based on shale gas horizontal well fracture network model

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105822298A (en) * 2016-04-25 2016-08-03 中石化石油工程技术服务有限公司 Method for acquiring absolute open flow of shale gas layer based on gas productivity index
CN105930932A (en) * 2016-04-25 2016-09-07 中石化石油工程技术服务有限公司 Gas index-based shale-gas-layer standardized open-flow capacity obtaining method
CN106503284A (en) * 2016-09-12 2017-03-15 中石化石油工程技术服务有限公司 Shale gas horizontal well horizontal segment gas-bearing formation aerogenesis evaluation method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105822298A (en) * 2016-04-25 2016-08-03 中石化石油工程技术服务有限公司 Method for acquiring absolute open flow of shale gas layer based on gas productivity index
CN105930932A (en) * 2016-04-25 2016-09-07 中石化石油工程技术服务有限公司 Gas index-based shale-gas-layer standardized open-flow capacity obtaining method
CN106503284A (en) * 2016-09-12 2017-03-15 中石化石油工程技术服务有限公司 Shale gas horizontal well horizontal segment gas-bearing formation aerogenesis evaluation method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
赵红燕: "川东南地区中深层含气页岩的录井评价", 《江汉石油职工大学学报》, vol. 30, no. 4, pages 1 - 4 *
陈建军: "基于产气指数的页岩气井无阻流量预测方法", 《江汉石油职工大学学报》, vol. 32, no. 2, pages 1 - 4 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113673771A (en) * 2021-08-24 2021-11-19 中国石油化工股份有限公司 Shale gas horizontal well fracturing segmentation method
CN113673771B (en) * 2021-08-24 2024-04-30 中国石油化工股份有限公司 Shale gas horizontal well fracturing segmentation method
CN113935161A (en) * 2021-09-29 2022-01-14 西南石油大学 Method for designing economic length of horizontal section of geothermal heating U-shaped butt-joint well
CN113935161B (en) * 2021-09-29 2024-06-04 西南石油大学 Method for designing economic length of horizontal section of geothermal heating U-shaped butt joint well
CN114693086A (en) * 2022-03-15 2022-07-01 湖南继善高科技有限公司 A fracturing evaluation method, system and storage medium based on electromagnetic fracturing monitoring
CN115330060A (en) * 2022-08-17 2022-11-11 中国科学院大学 Calculation method of productivity of shale oil horizontal well based on analysis of reservoir and engineering factors
CN119047171A (en) * 2024-08-20 2024-11-29 四川越盛能源集团有限公司 Pre-drilling EUR prediction method based on shale gas horizontal well fracture network model

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