This study presents the classification and prediction of severity for brittle rock failure,focusing on failure behaviors and excessive determination based on damage depth.The research utilizes extensive field survey d...This study presents the classification and prediction of severity for brittle rock failure,focusing on failure behaviors and excessive determination based on damage depth.The research utilizes extensive field survey data from the Shuangjiangkou Hydropower Station and previous research findings.Based on field surveys and previous studies,four types of brittle rock failure with different failure mechanisms are classified,and then a prediction method is proposed.This method incorporates two variables,i.e.Kv(modified rock mass integrity coefficient)and GSI(geological strength index).The prediction method is applied to the first layer excavation of the powerhouse cavern of Shuangjiangkou Hydropower Station.The results show that the predicted brittle rock failure area agrees with the actual failure area,demonstrating the method’s applicability.Next,it extends to investigate brittle rock failure in two locations.The first is the k0-890 m section of the traffic cavern,and the second one is at K0-64 m of the main powerhouse.The criterion-based prediction indicates a severity brittle rock failure in the K0-890 m section,and a moderate brittle rock failure in the K0-64 m section,which agrees with the actual occurrence of brittle rock failure in the field.The understanding and application of the prediction method using Kv and GSI are vital for implementing a comprehensive brittle rock failure prediction process in geological engineering.To validate the adaptability of this criterion across diverse tunnel projects,a rigorous verification process using statistical findings was conducted.The assessment outcomes demonstrate high accuracy for various tunnel projects,allowing establishment of the correlations that enable valuable conclusions regarding brittle rock failure occurrence.Further validation and refinement through field and laboratory testing,as well as simulations,can broaden the contribution of this method to safer and more resilient underground construction.展开更多
The Canglangpu Formation in the JT1 well area of the Sichuan Basin exhibits strong lateral heterogeneity and complex overpressure mechanisms, leading to ambiguous pore pressure distribution characteristics. Convention...The Canglangpu Formation in the JT1 well area of the Sichuan Basin exhibits strong lateral heterogeneity and complex overpressure mechanisms, leading to ambiguous pore pressure distribution characteristics. Conventional prediction methods, such as the Equivalent Depth Method, are either inapplicable or yield unsatisfactory results (e.g., Fillippone’s method), contributing to frequent drilling incidents like gas kick, overfl ow, and lost circulation, which hinder the safe and effi cient exploration of natural gas. To address these challenges, this paper integrates lithology, physical properties, and overpressure mechanisms of the Canglangpu Formation. From a petrophysical perspective, a pore pressure prediction model independent of lithology and overpressure mechanisms was developed by combining the poroelasticity theory, linear elastic Hooke’s Law, and Biot’s eff ective stress theory, with an analysis of the relationship between carbonate rock strain, external stress, and internal pore pressure. Unlike conventional methods, the model does not rely on the establishment of a normal compaction trend line. Pre-stack seismic inversion was applied to achieve 3D pore pressure prediction for the formation. Results indicate high accuracy, with a relative error of less than 5% compared to measured data, and strong consistency with actual drilling events. The proposed method provides robust technical support for pore pressure prediction in carbonate formations and drilling geological design.展开更多
Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale roc...Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.展开更多
The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical...The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R^(2)~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R^(2)~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R^(2)~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.展开更多
This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasing...This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasingly used for production objectives. The pre-stack seismic property studies include not only amplitude verse offset （AVO） but also the characteristics of other elastic property changes. In this paper, we analyze the elastic property parameters characteristics of gas- and wet-sands using data from four gas-sand core types. We found that some special elastic property parameters or combinations can be used to identify gas sands from water saturated sand. Thus, we can do reservoir interpretation and description using different elastic property data from the pre-stack seismic inversion processing. The pre- stack inversion method is based on the simplified Aki-Richard linear equation. The initial model can be generated from well log data and seismic and geologic interpreted horizons in the study area. The input seismic data is angle gathers generated from the common reflection gathers used in pre-stack time or depth migration. The inversion results are elastic property parameters or their combinations. We use a field data example to examine which elastic property parameters or combinations of parameters can most easily discriminate gas sands from background geology and which are most sensitive to pore-fluid content. Comparing the inversion results to well data, we found that it is useful to predict gas reservoirs using λ, λρ, λ/μ, and K/μ properties, which indicate the gas characteristics in the study reservoir.展开更多
In this paper,a quantitative seismic prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed in order to confirm highly brittle interva...In this paper,a quantitative seismic prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed in order to confirm highly brittle intervals and favorable fracturing zones in the shale reservoirs in the Jiaoshiba Block,Fuling shale gas field,Sichuan Basin.Firstly,the effect of structural compression stress on the brittleness characteristics of rocks were figured out by analyzing structures,mineral composition,development degree of fractures in cores,well logging and seismic data comprehensively.Secondly,according to the Rickman formula,a new brittleness index prediction model based on Young's modulus,Poisson's ratio and shear modulus×density was established by introducing the shear modulus which reflects lateral shear stress-strain.Finally,the quantitative prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed by virtue of the superiority of an elastic rock brittleness index method based on mineral composition to accurately calculate the brittleness index of a full hole.Field application shows that this technique is reliable,since its prediction results coincide with the calculated brittleness index of exploratory wells which are not used for modeling,with a relative error margin below 4%;and that the brittleness index of good shale of Upper Ordovician Wufeng Fm-Lower Silurian Longmaxi Fm in the Jiaoshiba Block increases from the top to the bottom and is stably distributed laterally.Particularly,the Wufeng-Longmaxi 1_(1) is the highest in brittleness index,so it is the most favorable interval for penetrating and fracturing of horizontal wells.展开更多
The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the o...The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.展开更多
Burial depth,thickness,total organic carbon(TOC)content,brittleness and fracture development of shale reservoirs are the main geologic indexes in the evaluation of sweet spots in shale gas plays.Taking the 2nd interva...Burial depth,thickness,total organic carbon(TOC)content,brittleness and fracture development of shale reservoirs are the main geologic indexes in the evaluation of sweet spots in shale gas plays.Taking the 2nd interval of Da'anzhai shale of the Lower Jurassic as the study object,a set of techniques in seismic prediction of sweet spots were developed based on special processing of seismic data and comprehensive analysis of various data based on these geologic indexes.First,logging and seismic responses of high quality shales were found out through fine calibration of shale reservoir location with seismogram,which was combined with seismic facies analysis to define the macroscopic distribution of the shale.Then,seismic impedance inversion and GR inversion were used to identify shale from limestone and sandstone.Based on statistical analysis of sensitive parameters such as TOC,the uranium log inversion technique was used to quantitatively predict TOC of a shale reservoir and the thickness of a high quality shale reservoir.After that,fracture prediction technique was employed to predict play fairways.Finally,the pre-stack joint P-wave and S-wave impedance inversion technique was adopted to identify shales with high brittleness suitable for hydraulic fracturing.These seismic prediction techniques have been applied in sorting out sweet spots in the 2nd interval of the Da'anzhai shale play of the Yuanba area,and the results provided a sound basis for the optimization of horizontal well placement and hydraulic fracturing.展开更多
Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid cha...Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid characterization. In this paper, starting with the exact Zoeppritz equation that relates P-and S-wave moduli, a coefficient that describes the reflections of P-and converted waves is established. This method effectively avoids error introduced by approximations or indirect calculations, thus improving the accuracy of the inversion results. Considering that the inversion problem is ill-posed and that the forward operator is nonlinear, prior constraints on the model parameters and modified low-frequency constraints are also introduced to the objective function to make the problem more tractable. This modified objective function is solved over many iterations to continuously optimize the background values of the velocity ratio, which increases the stability of the inversion process. Tests of various models show that the method effectively improves the accuracy and stability of extracting P and S-wave moduli from underdetermined data. This method can be applied to provide inferences for reservoir exploration and fluid extraction.展开更多
基金the National Natural Science Foundation of China(Nos.41825018,42141009)the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)(No.2019QZKK0904).
文摘This study presents the classification and prediction of severity for brittle rock failure,focusing on failure behaviors and excessive determination based on damage depth.The research utilizes extensive field survey data from the Shuangjiangkou Hydropower Station and previous research findings.Based on field surveys and previous studies,four types of brittle rock failure with different failure mechanisms are classified,and then a prediction method is proposed.This method incorporates two variables,i.e.Kv(modified rock mass integrity coefficient)and GSI(geological strength index).The prediction method is applied to the first layer excavation of the powerhouse cavern of Shuangjiangkou Hydropower Station.The results show that the predicted brittle rock failure area agrees with the actual failure area,demonstrating the method’s applicability.Next,it extends to investigate brittle rock failure in two locations.The first is the k0-890 m section of the traffic cavern,and the second one is at K0-64 m of the main powerhouse.The criterion-based prediction indicates a severity brittle rock failure in the K0-890 m section,and a moderate brittle rock failure in the K0-64 m section,which agrees with the actual occurrence of brittle rock failure in the field.The understanding and application of the prediction method using Kv and GSI are vital for implementing a comprehensive brittle rock failure prediction process in geological engineering.To validate the adaptability of this criterion across diverse tunnel projects,a rigorous verification process using statistical findings was conducted.The assessment outcomes demonstrate high accuracy for various tunnel projects,allowing establishment of the correlations that enable valuable conclusions regarding brittle rock failure occurrence.Further validation and refinement through field and laboratory testing,as well as simulations,can broaden the contribution of this method to safer and more resilient underground construction.
基金supported by innovation consortium project of China Petroleum and Southwest Petroleum University (No.2020CX010201)Sichuan Science and Technology Program (No. 2024NSFSC0081)。
文摘The Canglangpu Formation in the JT1 well area of the Sichuan Basin exhibits strong lateral heterogeneity and complex overpressure mechanisms, leading to ambiguous pore pressure distribution characteristics. Conventional prediction methods, such as the Equivalent Depth Method, are either inapplicable or yield unsatisfactory results (e.g., Fillippone’s method), contributing to frequent drilling incidents like gas kick, overfl ow, and lost circulation, which hinder the safe and effi cient exploration of natural gas. To address these challenges, this paper integrates lithology, physical properties, and overpressure mechanisms of the Canglangpu Formation. From a petrophysical perspective, a pore pressure prediction model independent of lithology and overpressure mechanisms was developed by combining the poroelasticity theory, linear elastic Hooke’s Law, and Biot’s eff ective stress theory, with an analysis of the relationship between carbonate rock strain, external stress, and internal pore pressure. Unlike conventional methods, the model does not rely on the establishment of a normal compaction trend line. Pre-stack seismic inversion was applied to achieve 3D pore pressure prediction for the formation. Results indicate high accuracy, with a relative error of less than 5% compared to measured data, and strong consistency with actual drilling events. The proposed method provides robust technical support for pore pressure prediction in carbonate formations and drilling geological design.
文摘Due to the huge differences between the unconventional shale and conventional sand reservoirs in many aspects such as the types and the characteristics of minerals,matrix pores and fluids,the construction of shale rock physics model is significant for the exploration and development of shale reservoirs.To make a better characterization of shale gas-bearing reservoirs,we first propose a new but more suitable rock physics model to characterize the reservoirs.We then use a well A to demonstrate the feasibility and reliability of the proposed rock physics model of shale gas-bearing reservoirs.Moreover,we propose a new brittleness indicator for the high-porosity and organic-rich shale gas-bearing reservoirs.Based on the parameter analysis using the constructed rock physics model,we finally compare the new brittleness indicator with the commonly used Young’s modulus in the content of quartz and organic matter,the matrix porosity,and the types of filled fluids.We also propose a new shale brittleness index by integrating the proposed new brittleness indicator and the Poisson’s ratio.Tests on real data sets demonstrate that the new brittleness indicator and index are more sensitive than the commonly used Young’s modulus and brittleness index for the high-porosity and high-brittleness shale gas-bearing reservoirs.
文摘The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R^(2)~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R^(2)~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R^(2)~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.
基金supported by the National Basic Priorities Program "973" Project (Grant No.2007CB209600)China Postdoctoral Science Foundation Funded Project
文摘This is a case study of the application of pre-stack inverted elastic parameters to tight-sand reservoir prediction. With the development of oil and gas exploration, pre-stack data and inversion results are increasingly used for production objectives. The pre-stack seismic property studies include not only amplitude verse offset （AVO） but also the characteristics of other elastic property changes. In this paper, we analyze the elastic property parameters characteristics of gas- and wet-sands using data from four gas-sand core types. We found that some special elastic property parameters or combinations can be used to identify gas sands from water saturated sand. Thus, we can do reservoir interpretation and description using different elastic property data from the pre-stack seismic inversion processing. The pre- stack inversion method is based on the simplified Aki-Richard linear equation. The initial model can be generated from well log data and seismic and geologic interpreted horizons in the study area. The input seismic data is angle gathers generated from the common reflection gathers used in pre-stack time or depth migration. The inversion results are elastic property parameters or their combinations. We use a field data example to examine which elastic property parameters or combinations of parameters can most easily discriminate gas sands from background geology and which are most sensitive to pore-fluid content. Comparing the inversion results to well data, we found that it is useful to predict gas reservoirs using λ, λρ, λ/μ, and K/μ properties, which indicate the gas characteristics in the study reservoir.
文摘In this paper,a quantitative seismic prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed in order to confirm highly brittle intervals and favorable fracturing zones in the shale reservoirs in the Jiaoshiba Block,Fuling shale gas field,Sichuan Basin.Firstly,the effect of structural compression stress on the brittleness characteristics of rocks were figured out by analyzing structures,mineral composition,development degree of fractures in cores,well logging and seismic data comprehensively.Secondly,according to the Rickman formula,a new brittleness index prediction model based on Young's modulus,Poisson's ratio and shear modulus×density was established by introducing the shear modulus which reflects lateral shear stress-strain.Finally,the quantitative prediction technique of multi-parameter shale brittleness index suitable for the environment with complex structural stress was developed by virtue of the superiority of an elastic rock brittleness index method based on mineral composition to accurately calculate the brittleness index of a full hole.Field application shows that this technique is reliable,since its prediction results coincide with the calculated brittleness index of exploratory wells which are not used for modeling,with a relative error margin below 4%;and that the brittleness index of good shale of Upper Ordovician Wufeng Fm-Lower Silurian Longmaxi Fm in the Jiaoshiba Block increases from the top to the bottom and is stably distributed laterally.Particularly,the Wufeng-Longmaxi 1_(1) is the highest in brittleness index,so it is the most favorable interval for penetrating and fracturing of horizontal wells.
基金supported by National Basic Research Program(2006CB202304)of Chinaco-supported by the National Basic Research Program of China(Grant No.2011CB201103)the National Science and Technology Major Project of China(Grant No.2011ZX05004003)
文摘The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.
基金Scientific research project of China Petroleum&Chemical Corporation(No.P13129).
文摘Burial depth,thickness,total organic carbon(TOC)content,brittleness and fracture development of shale reservoirs are the main geologic indexes in the evaluation of sweet spots in shale gas plays.Taking the 2nd interval of Da'anzhai shale of the Lower Jurassic as the study object,a set of techniques in seismic prediction of sweet spots were developed based on special processing of seismic data and comprehensive analysis of various data based on these geologic indexes.First,logging and seismic responses of high quality shales were found out through fine calibration of shale reservoir location with seismogram,which was combined with seismic facies analysis to define the macroscopic distribution of the shale.Then,seismic impedance inversion and GR inversion were used to identify shale from limestone and sandstone.Based on statistical analysis of sensitive parameters such as TOC,the uranium log inversion technique was used to quantitatively predict TOC of a shale reservoir and the thickness of a high quality shale reservoir.After that,fracture prediction technique was employed to predict play fairways.Finally,the pre-stack joint P-wave and S-wave impedance inversion technique was adopted to identify shales with high brittleness suitable for hydraulic fracturing.These seismic prediction techniques have been applied in sorting out sweet spots in the 2nd interval of the Da'anzhai shale play of the Yuanba area,and the results provided a sound basis for the optimization of horizontal well placement and hydraulic fracturing.
基金supported by the National Science and Technology Major Project(No.2016ZX05047-002-001)
文摘Joint PP–PS inversion offers better accuracy and resolution than conventional P-wave inversion. P-and S-wave elastic moduli determined through data inversions are key parameters for reservoir evaluation and fluid characterization. In this paper, starting with the exact Zoeppritz equation that relates P-and S-wave moduli, a coefficient that describes the reflections of P-and converted waves is established. This method effectively avoids error introduced by approximations or indirect calculations, thus improving the accuracy of the inversion results. Considering that the inversion problem is ill-posed and that the forward operator is nonlinear, prior constraints on the model parameters and modified low-frequency constraints are also introduced to the objective function to make the problem more tractable. This modified objective function is solved over many iterations to continuously optimize the background values of the velocity ratio, which increases the stability of the inversion process. Tests of various models show that the method effectively improves the accuracy and stability of extracting P and S-wave moduli from underdetermined data. This method can be applied to provide inferences for reservoir exploration and fluid extraction.
