Quantitative evaluation method of sweet spot quality in conglomerate horizontal wells based on logging while drilling data

Abstract

The present invention provides a method for quantitatively evaluating the sweet spot quality of conglomerate horizontal wells based on logging-while-drilling (LWD) data. Because some wells lack completion electrical logging data and dipole acoustic wave data, density, P-wave transit time, and S-wave transit time data cannot be directly obtained. LWD and gas logging data must be used to model and effectively model density, acoustic wave, and S-wave transit time. Based on the calculated density, acoustic wave, and S-wave transit time, geological parameters such as porosity, saturation, and permeability can be calculated. Based on S-wave transit time, density, and P-wave transit time, reservoir engineering parameters such as Young's modulus, Poisson's ratio, brittleness index, and maximum and minimum principal stresses can be calculated. The quality of the horizontal well sweet spot can be quantitatively evaluated by comprehensively utilizing the geological sweet spot and engineering sweet spot parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of comprehensive evaluation of tight oil and gas geology, and in particular to a method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data. Background Art

[0002] In geological exploration, the "sweet spot" in a shale formation is defined as a region with good reservoir geology and amenability to fracturing. This sweet spot is crucial for shale development, as identifying it can help reduce shale exploration and development costs and increase the productivity of gas-bearing shale formations.

[0003] Exploration breakthroughs have been made in some areas in recent years, demonstrating promising resource prospects. However, compared to other tight oil fields in China, reservoirs in some regions have complex geological conditions, posing challenges in improving drilling efficiency. In some basins, horizontal wells, influenced by wellbore conditions and cost-efficiency reduction strategies, have been drilled without electrical logging in the horizontal section. These wells typically undergo gas logging and geosteering while drilling (LWD). Gas logging and rock cuttings data are acquired in the horizontal section, while LWD geosteering provides gamma-ray and LWD electromagnetic logging resistivity data. Subsequent staged and clustered fracturing in horizontal wells requires comprehensive reservoir sweet spot evaluation data (including geological and engineering sweet spots). However, for special reasons, completed horizontal wells are not subjected to completion logging, resulting in no data on formation compensation density, compensated acoustic waves, or formation shear waves in the horizontal section. Therefore, a study on sweet spot evaluation in the Mahu tight conglomerate is warranted based on large-scale gas logging and LWD data from multiple wells. Horizontal well logging evaluation technology is currently a key technology for developing unconventional oil and gas reservoirs. (Patent No. CN108875122A) has developed an artificial intelligence method and system for analyzing geological parameters using LWD data. This method calculates geological parameters from downhole logging data to obtain formation structural and electrical parameters that describe geological occurrence. This significantly reduces the amount of data transmitted during drilling and allows for visualization and quantification of geological information reflected in logging data, which is of great significance for geosteering and logging interpretation. The conference paper, "Sweet Spot Evaluation Based on Gas Logging and LWD Data," briefly introduced density and acoustic oil saturation curve fitting.

[0004] However, the parameter evaluation in patent CN108875122A has the following shortcomings: 1. It only considers reservoir geological parameters. Reservoir evaluation now adopts integrated geological and engineering evaluation, and this patent lacks the calculation of engineering parameters; 2. Parameter calculation based solely on LWD data may not be detailed enough, and the application of gas logging data is lacking; 3. The calculation of geological parameters based on LWD data in the patent is based on the premise that the well has completion electrical logging data, and lacks the interpretation and evaluation content of horizontal wells that do not have completion electrical logging due to complex well conditions and cost savings.

[0005] The conference paper, "Sweet Spot Evaluation Based on Gas Logging and LWD Data," briefly introduced density and acoustic oil saturation curve fitting, lacking shear wave velocity fitting, essential for engineering sweet spot evaluation, making it incomplete. This patented invention utilizes gas logging and LWD data to establish acoustic, density, and shear wave velocity models to conduct a detailed evaluation of geological and engineering sweet spots in completed horizontal wells, saving logging costs and enabling interpretation of results that meet the requirements for later staged and clustered fracturing. Summary of the Invention

[0006] In response to the problems existing in the prior art, the present invention provides a method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data. This method uses existing logging data to invert unknown logging curves. Application examples have shown that the evaluation of reservoir sweet spots is highly accurate and effective, and has certain application value in mine practice.

[0007] The present invention is achieved through the following technical solutions:

[0008] A method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data comprises the following steps:

[0009] Step 1: Obtain the parameters of logging while drilling and gas logging, and standardize the parameters;

[0010] Step 2: Perform data correlation analysis on the standardized parameters, and select relevant data to establish a horizontal well density model and a shear wave prediction model through multivariate nonlinear fitting;

[0011] Among them, the standardized parameters are used for density and gas logging and LWD data analysis, including gas logging data and LWD data. The gas logging data includes C1 methane, C2 ethane, C3 propane, IC4 isobutane, IC5 isopentane, NC4 normal butane, and QT total hydrocarbons; the LWD data includes GR LWD natural gamma, P34H LWD phase shift resistance, TR: 34in, high frequency and P28H LWD phase shift resistance, TR: 28in, high frequency;

[0012] Among them, the standardized parameters were used for correlation analysis between measured shear wave time difference and conventional logging data, and the relevant data selected included density DEN, compression wave time difference AC, deep lateral resistivity RT, flushing zone resistivity RXO, natural gamma ray GR and logging depth DEPTH;

[0013] The calculation formula of the horizontal well density model is as follows:

[0014] ;

[0015] Where DEN is the density value;

[0016] The calculation formula of the shear wave prediction model is as follows:

[0017] ;

[0018] Wherein, DTSM is the shear wave time difference; Depth is the logging depth; GR is the natural gamma ray; RXO is the flushing zone resistivity; RT is the deep lateral resistivity; AC is the compressional wave time difference.

[0019] Step 3: After calculating the density value using the horizontal well density model, the reservoir geological parameters are calculated; the shear wave time difference is calculated using the selected longitudinal wave time difference combined with the shear wave prediction model to calculate the reservoir engineering parameters;

[0020] Step 4: Comprehensively utilize reservoir engineering parameters and reservoir geological parameters to quantitatively evaluate the quality of the horizontal well sweet spot.

[0021] Preferably, in step 1, the parameters are standardized using the calculation formula:

[0022] Set the variable to be standardized: X=(x ij ) n×p ;

[0023] Standardize the variables that need to be standardized to obtain the logging parameter values:

[0024] (i=1, 2,…,n; j=1, 2,…,p);

[0025] in, is x j The sample mean of ; is x j The sample standard deviation of , X is the logging parameter; is the normalized logging parameter value; x ij is the logging parameter value before normalization.

[0026] Furthermore, through correlation analysis of density and gas logging and LWD data, relevant data including P34H LWD deep resistivity, P28H LWD shallow resistivity, GR LWD gamma, QT total hydrocarbons, NC5 n-pentane, NC4 n-butane and IC4 isobutane were selected.

[0027] Furthermore, the density value obtained by calculating the horizontal well density model is sequentially used to calculate the porosity model, oil saturation model and permeability to obtain the reservoir geological parameters of porosity, oil saturation and permeability.

[0028] Furthermore, the shear wave time difference is calculated by combining the selected longitudinal wave time difference with the shear wave prediction model, and the reservoir engineering parameters such as Young's modulus, Poisson's ratio, brittleness index and maximum and minimum horizontal well principal stresses can be calculated based on the shear wave time difference.

[0029] Preferably, in step 4, the method for quantitatively evaluating the quality of the horizontal well sweet spot by comprehensively utilizing reservoir engineering parameters and reservoir geological parameters is as follows:

[0030] By fitting the density using the horizontal well density model, the reservoir porosity can be calculated, and then the reservoir oil saturation can be calculated, the geological sweet spot of the reservoir can be evaluated, the oil-bearing porosity parameters can be established using the porosity and oil saturation, and the oil layers can be divided into one, two, and three categories; by fitting the P-wave time difference, S-wave time difference, and density using the S-wave prediction model, the rock physical parameters Young's modulus, Poisson's ratio, brittleness index, and maximum and minimum horizontal principal stress parameters can be calculated, the engineering sweet spot of the reservoir can be evaluated, and the feasibility of the oil layer can be classified based on the engineering sweet spot and the brittleness index and minimum principal stress.

[0031] Compared with the prior art, the present invention has the following beneficial technical effects:

[0032] The present invention provides a method for quantitatively evaluating the quality of conglomerate horizontal well sweet spots based on logging-while-drilling (LWD) data. Because some wells lack completion electrical logging data and dipole acoustic wave data, density, P-wave time difference, and S-wave time difference data cannot be directly obtained. LWD and gas logging data must be used to model and effectively model density, acoustic wave, and S-wave time differences. Based on the calculated density, acoustic wave, and S-wave time differences, geological parameters such as porosity, saturation, and permeability can be calculated. Based on S-wave time difference, density, and P-wave time difference, reservoir engineering parameters such as Young's modulus, Poisson's ratio, brittleness index, and maximum and minimum principal stresses can be calculated. The quality of horizontal well sweet spots can be quantitatively evaluated by comprehensively utilizing geological sweet spot and engineering sweet spot parameters.

[0033] Furthermore, based on the actual data acquisition situation, we selected well-logged horizontal wells surrounding uncompleted electrical logging wells and established a multivariate nonlinear relationship between gas logging and LWD data and reservoir density and P-wave time difference based on big data analysis principles. We then used multiple regression methods to predict reservoir acoustic waves and density in the uncompleted electrical logging wells. We then used multivariate nonlinear fitting methods to establish a shear wave prediction model, based on which we conducted a comprehensive sweet spot evaluation for the Mahu tight conglomerate. This method of inverting unknown logging curves using existing logging data has demonstrated high accuracy and significant results in reservoir sweet spot evaluation, demonstrating its value in field practice. This technical approach is suitable for horizontal wells lacking completed electrical logging data.

[0034] Furthermore, the shear wave prediction model can save the cost of dipole acoustic logging. The density and acoustic wave model based on the logging and drilling data can save the cost of completion logging, effectively reducing the cost and having a wide range of applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1This is a flow chart of the method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data in the present invention;

[0036] Figure 2 This is a comparison chart of the calculated and fitted oil saturation of MaHW6105 in the present invention;

[0037] Figure 3 This is a comparison chart of the measured density and fitted density of MaHW6105 in the present invention;

[0038] Figure 4 This is a comparison chart of the measured sound waves and the fitted sound waves of MaHW6105 in the present invention;

[0039] Figure 5 This is the multivariate nonlinear fitting of shear wave time difference in the Ma 18 well area in the present invention;

[0040] Figure 6 This is a comparison chart of the comprehensive interpretation and fitting interpretation of MaHW6110 in the present invention;

[0041] Figure 7 This is a comprehensive explanation result diagram of MaHW6109 in the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] The present invention is described in further detail below with reference to the accompanying drawings:

[0045] In one embodiment of the present invention, a method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data is provided. This method utilizes existing logging data to invert unknown logging curves. Application examples have shown that the evaluation of reservoir sweet spots is highly accurate and effective, and has certain application value in mining practice.

[0046] The first horizontal well drilled on a Junggar Basin drilling platform underwent both mid-well and completion logging. Only gamma ray (GR) logging was performed on the remaining wells on the same platform during mid-well logging. No completion logging was performed on the well section from the bottom of the well to 30 m above the top of the Baikouquan Formation. Data from vertical wells surrounding the horizontal well, combined with the horizontal well's geosteering trajectory parameters, were used to identify the interval encountered by the vertical well. The reservoir logging characteristics of the interval encountered by the vertical well were compared with those fitted from the untested horizontal wells to perform quality control on the predicted data for the untested horizontal interval.

[0047] Using logging while drilling data to directly fit oil saturation, we first verified it on a vertical well. We selected gamma (GR), deep resistivity (RT), and shallow resistivity (RI) from the M1 well in the Mahu area and established a multivariate regression model with oil saturation interpreted based on actual completion electrical logging data. The model was applied to the horizontal well M125, and it can be seen that the error between the fitted oil saturation and the original calculated value is very small. Figure 2 Therefore, a model is established based on the completion electrical logging, horizontal well gas logging and logging while drilling data from multiple well platforms, and the oil saturation of horizontal wells can be predicted without a logging series.

[0048] Fitting oil saturation to gas logging and LWD data can identify geological sweet spots, but without density and acoustic data, it's impossible to assess engineering sweet spots. These two parameters in the M1 reservoir section are correlated with GR, RT, and gas logging data to generate density and acoustic curves. Density and acoustic characteristic parameters were selected to best reflect the amplitude and morphological variations of the logging curves, as well as the structural characteristics of the geological variables themselves.

[0049] Based on the above, the quantitative evaluation method of sweet spot quality in conglomerate horizontal wells based on logging while drilling data is Figure 1 As shown, the following steps are included:

[0050] Step 1: Obtain the parameters of logging while drilling and gas logging, and standardize the parameters;

[0051] Step 2: Perform data correlation analysis on the standardized parameters, and select relevant data to establish a horizontal well density model and a shear wave prediction model through multivariate nonlinear fitting;

[0052] Step 3: After calculating the density value using the horizontal well density model, the reservoir geological parameters are calculated; the shear wave time difference is calculated using the selected longitudinal wave time difference combined with the shear wave prediction model to calculate the reservoir engineering parameters;

[0053] Step 4: Comprehensively utilize reservoir engineering parameters and reservoir geological parameters to quantitatively evaluate the quality of the horizontal well sweet spot.

[0054] In step 1, since all parameters have different dimensions and the actual test magnitudes vary greatly, the logging parameters need to be standardized first. Let the variable to be standardized be X = (x ij ) n×p , the standardized logging parameter values ​​are: (i=1, 2, ..., n; j=1, 2, ..., p) where, is x j The sample mean of ; is x j The sample standard deviation of ; X is the logging parameter; is the normalized logging parameter value; x ij is the logging parameter value before normalization.

[0055] In step 2, the standardized parameters were used to analyze density, gas logging, and LWD data. These data included gas logging data (C1 methane, C2 ethane, C3 propane, IC4 isobutane, IC5 isopentane, NC4 normal butane, and QT total hydrocarbons). The LWD data included GR natural gamma, P34H phase-shift resistivity (TR: 34 in, high frequency), and P28H phase-shift resistivity (TR: 28 in, high frequency), as shown in Table 1. Through correlation analysis, seven variables with a Pearson correlation greater than 0.4 with density were selected as the main analysis parameters for modeling: deep resistivity (P34H), shallow resistivity (P28H), gamma (GR), total hydrocarbons (QT), normal pentane (NC5), normal butane (NC4), and isobutane (IC4).

[0056]

[0057] Table 1 Correlation analysis between density and gas logging and LWD data

[0058] A density and acoustic wave fitting model for horizontal well L04 was established using multiple regression methods and applied to the adjacent horizontal well L05. The closest measured horizontal well was selected rather than using data from the entire area, considering that reservoir variations between wells can be significant. Using only closely spaced horizontal wells minimizes the impact of reservoir variations on prediction results. Using vertical well data for quality control ensures the quality of predictions while minimizing the impact of model discrepancies caused by using vertical well models to evaluate horizontal well reservoirs.

[0059] The calculation formula of the horizontal well density model is as follows:

[0060] ;

[0061] Where DEN is the density value.

[0062] The calculation formula of the horizontal well P-wave time difference model is as follows:

[0063]

[0064] Where AC is the longitudinal wave time difference value.

[0065] Specifically, after obtaining the density and longitudinal wave time difference values. Figure 3 and Figure 4 As shown, the density value calculated by the horizontal well density model is sequentially used to calculate the porosity model, oil saturation model and permeability to obtain the reservoir geological parameters of porosity, oil saturation and permeability.

[0066] In step 2, the shear wave prediction approach uses conventional well logging data and a small amount of dipole shear wave data to establish a corresponding model to predict shear wave time difference. A shear wave prediction model has been developed for the Junggar Basin, but its practicality is limited. This study area allows for targeted optimization. First, a big data correlation analysis is performed based on the well logging data, and a prediction model suitable for the study area is established through multivariate nonlinear fitting.

[0067] The standardized parameters were used for correlation analysis between measured shear-wave time difference and conventional logging data. The relevant data selected included density DEN, compressional-wave time difference AC, deep lateral resistivity RT, flushing zone resistivity RXO, natural gamma ray GR, and logging depth DEPTH, as shown in Table 2.

[0068]

[0069] Table 2 Correlation analysis between measured shear wave time difference and conventional logging data

[0070] The shear wave prediction model was established using the multivariate nonlinear method with a correlation coefficient of 0.85. When applied to the Ma 18 well area, it can be seen that the shear wave values ​​calculated by this model are more accurate than those calculated by the original shear wave prediction model in the Junggar Basin. Figure 5 shown.

[0071] Specifically, the calculation formula of the shear wave prediction model is as follows:

[0072] ;

[0073] Wherein, DTSM is the shear wave time difference; Depth is the logging depth; GR is the natural gamma ray; RXO is the flushing zone resistivity; RT is the deep lateral resistivity; AC is the compressional wave time difference.

[0074] The shear wave time difference is calculated by combining the selected longitudinal wave time difference with the shear wave prediction model. According to the shear wave time difference, the reservoir engineering parameters such as Young's modulus, Poisson's ratio, brittleness index and maximum and minimum horizontal well principal stress can be calculated.

[0075] The method for quantitatively evaluating the quality of horizontal well sweet spots by comprehensively utilizing reservoir engineering parameters and reservoir geological parameters is as follows: Figure 6 and Figure 7 As shown in the figure, by fitting the density, the reservoir porosity can be calculated, and then the reservoir oil saturation can be calculated, the reservoir geological sweet spot evaluation can be carried out, the oil porosity parameters can be established using the porosity and oil saturation, and the oil layer can be divided into one, two, and three categories; by fitting the longitudinal wave time difference, shear wave time difference, and density, the rock physical parameters such as Young's modulus, Poisson's ratio, brittleness index, and maximum and minimum horizontal principal stresses can be calculated, and the reservoir engineering sweet spot can be evaluated. The feasibility of the oil layer can be classified based on the engineering sweet spot combined with the brittleness index and minimum principal stress.

[0076] The shear wave prediction model in the present invention can save the cost of dipole acoustic logging. The density and acoustic wave model based on the logging and drilling data can save the cost of completion logging. In the Mahu area, the logging cost of a single well was more than 800,000 yuan.

[0077] This invention can be extended to all blocks of the Mahu tight conglomerate oil reservoir in the next three years, reducing well logging investment and creating economic benefits of 350 million yuan.

[0078] The invention can avoid unnecessary investment to a certain extent. The innovative results can be promoted in large sandstone, volcanic rock and carbonate reservoirs in the next 3-5 years, with broad application prospects and immeasurable economic benefits.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data, characterized in that: The steps include: Step 1: Obtain the parameters of logging while drilling and gas logging, and standardize the parameters; Step 2: Perform data correlation analysis on the standardized parameters, and select relevant data to establish a horizontal well density model and a shear wave prediction model through multivariate nonlinear fitting; Among them, the standardized parameters are used for density and gas logging and LWD data analysis, including gas logging data and LWD data. The gas logging data includes C1 methane, C2 ethane, C3 propane, IC4 isobutane, IC5 isopentane, NC4 normal butane, and QT total hydrocarbons; the LWD data includes GR LWD natural gamma, P34H LWD phase shift resistance, TR: 34in, high frequency and P28H LWD phase shift resistance, TR: 28in, high frequency; Among them, the standardized parameters were used for correlation analysis between measured shear wave time difference and conventional logging data, and the relevant data selected included density DEN, compression wave time difference AC, deep lateral resistivity RT, flushing zone resistivity RXO, natural gamma ray GR and logging depth DEPTH; The calculation formula of the horizontal well density model is as follows: ； Where DEN is the density value; The calculation formula of the shear wave prediction model is as follows: ； Wherein, DTSM is the shear wave time difference; Depth is the logging depth; GR is the natural gamma ray; RXO is the flushing zone resistivity; RT is the deep lateral resistivity; AC is the compressional wave time difference; Step 3: After calculating the density value using the horizontal well density model, the reservoir geological parameters are calculated; the shear wave time difference is calculated using the selected longitudinal wave time difference combined with the shear wave prediction model to calculate the reservoir engineering parameters; Step 4: Comprehensively utilize reservoir engineering parameters and reservoir geological parameters to quantitatively evaluate the quality of the horizontal well sweet spot.

2. The method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data according to claim 1, characterized in that: In step 1, the parameters are standardized and calculated using the formula: Set the variable to be standardized: X=(x ij ) n×p ; Standardize the variables that need to be standardized to obtain the logging parameter values: (i=1,2,…,n;j=1,2,…,p); in, is x j The sample mean of ; is x j The sample standard deviation of , X is the logging parameter; is the normalized logging parameter value; x ij is the logging parameter value before normalization.

3. The method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data according to claim 1, characterized in that: Through correlation analysis of density and gas logging and LWD data, relevant data including P34H LWD deep resistivity, P28H LWD shallow resistivity, GR LWD gamma, QT total hydrocarbons, NC5 n-pentane, NC4 n-butane and IC4 isobutane were selected.

4. The method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data according to claim 1, characterized in that: The density value obtained by calculating the horizontal well density model is sequentially used to calculate the porosity model, oil saturation model and permeability to obtain the reservoir geological parameters of porosity, oil saturation and permeability.

5. The method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data according to claim 1, characterized in that: The shear wave time difference is calculated by combining the selected longitudinal wave time difference with the shear wave prediction model. According to the shear wave time difference, the reservoir engineering parameters such as Young's modulus, Poisson's ratio, brittleness index and maximum and minimum horizontal well principal stress can be calculated.

6. The method for quantitatively evaluating the quality of sweet spots in conglomerate horizontal wells based on logging while drilling data according to claim 1, characterized in that: In step 4, the method for quantitatively evaluating the quality of the horizontal well sweet spot by comprehensively utilizing reservoir engineering parameters and reservoir geological parameters is as follows: By fitting the density using the horizontal well density model, we can calculate the reservoir porosity and then the reservoir oil saturation, conduct a geological sweet spot evaluation on the reservoir, establish the oil-bearing porosity parameters using the porosity and oil saturation, and classify the oil layers into one, two, and three categories. By fitting the P-wave time difference, S-wave time difference, and density using the S-wave prediction model, we can calculate the rock physical parameters Young's modulus, Poisson's ratio, brittleness index, and maximum and minimum horizontal principal stress parameters, conduct an engineering sweet spot evaluation on the reservoir, and classify the oil layers into feasibility categories based on the engineering sweet spot and the brittleness index and minimum principal stress.

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