The formation water sample in oil and gas fields may be polluted in processes of testing, trial production, collection, storage, transportation and analysis, making the properties of formation water not be reflected t...The formation water sample in oil and gas fields may be polluted in processes of testing, trial production, collection, storage, transportation and analysis, making the properties of formation water not be reflected truly. This paper discusses identification methods and the data credibility evaluation method for formation water in oil and gas fields of petroliferous basins within China. The results of the study show that: (1) the identification methods of formation water include the basic methods of single factors such as physical characteristics, water composition characteristics, water type characteristics, and characteristic coefficients, as well as the comprehensive evaluation method of data credibility proposed on this basis, which mainly relies on the correlation analysis sodium chloride coefficient and desulfurization coefficient and combines geological background evaluation;(2) The basic identifying methods for formation water enable the preliminary identification of hydrochemical data and the preliminary screening of data on site, the proposed comprehensive method realizes the evaluation by classifying the CaCl2-type water into types A-I to A-VI and the NaHCO3-type water into types B-I to B-IV, so that researchers can make in-depth evaluation on the credibility of hydrochemical data and analysis of influencing factors;(3) When the basic methods are used to identify the formation water, the formation water containing anions such as CO_(3)^(2-), OH- and NO_(3)^(-), or the formation water with the sodium chloride coefficient and desulphurization coefficient not matching the geological setting, are all invaded with surface water or polluted by working fluid;(4) When the comprehensive method is used, the data credibility of A-I, A-II, B-I and B-II formation water can be evaluated effectively and accurately only if the geological setting analysis in respect of the factors such as formation environment, sampling conditions, condensate water, acid fluid, leaching of ancient weathering crust, and ancient atmospheric fresh water, is combined, although such formation water is believed with high credibility.展开更多
Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pres...Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm^(3),with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications.展开更多
Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics data...Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database （NIMZDB） to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues： ① data accuracy： integrity, logic consistency, attribute, spatial and time accuracy; ② management of both attribute and spatial data in the same system;③ transforming data between MapGIS and ArcGIS; ④ data sharing and security; ⑤ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using AreSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.展开更多
To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a four...To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.展开更多
A new possible data archive format for storing huge amounts of data for EAST control anddata acquisition system is presented. This new general-purpose data archive format is network-transparent, i.e, machine-independe...A new possible data archive format for storing huge amounts of data for EAST control anddata acquisition system is presented. This new general-purpose data archive format is network-transparent, i.e, machine-independent and has been implemented in terms of XDR （eXternal Data Representation）. We test this format by using EFIT （Equilibrium Fitting） code on different operation systems, namely Linux and Windows, different processors, namely Sun and Pc, and different programs, namely in Fortran and C language. It can be easily used by different computers and different programming languages.展开更多
A typical building project has a long life in the maintenance stage. Also, the cost at this stage is enormously huge compared to planning, design and construction phases. In the earlier stage, which is planning or des...A typical building project has a long life in the maintenance stage. Also, the cost at this stage is enormously huge compared to planning, design and construction phases. In the earlier stage, which is planning or design phase, however, many project participants put little emphasis on the maintenance information. As a result, important maintenance data is missing and erroneously feedback to the 3D/BIM model. This research provides a generic process model for maintenance information management for building facilities. The authors have identified that there exist most-frequently used information areas: checking information, material information, equipment information, supplier information, and maintenance history information. Each information area should be embedded in the BIM model in order to effectively feedback to the operation and maintenance stage in the project. Thus, the study has proposed a novel data format structure which can effectively link the 3D/BIM object with the maintenance data. The demonstration project shows how the data format structure is used. The contribution of this study is to provide guidance to a project practitioner by step-by-step approach in dealing with the significant maintenance information in the earlier stage of the construction project.展开更多
New liquid-liquid equilibrium data for polyethylene glycol （PEG） 3000 ＋ CHO2K ＋ H20 systems were measured at 298.15 K and pH values of 7.95, 8.40 and 9.98. It was found that an increase in pH caused the binodal cu...New liquid-liquid equilibrium data for polyethylene glycol （PEG） 3000 ＋ CHO2K ＋ H20 systems were measured at 298.15 K and pH values of 7.95, 8.40 and 9.98. It was found that an increase in pH caused the binodal curve to be displaced downward and the two-phase region to expand. Accordingly, the binodal curve was adjusted to the Pirdashti equation and the tie-line compositions were correlated using the Othmer-Tobias, Bancroft and Hand equations. The study measured the refractive index and densities of several homogeneous binary and ternary solutions. The solutions were used for calibration within a range of 0% to 30% of the mass of the PEG and potassium formate. The density and refractive index data show a linear variation with the mass fraction of the polymer and the salt. The effect of pH on the binodal, tie-line lengths （TLL） and slope of the tie-line （STL） in the systems was exam- ined. It was found that an increase in pH increased the TLL and decreased the STL It was observed that the density of the aqueous two-phase system was influenced by the TLL The difference in density between phases （△p） increased as the TLL and pH increased. It was found that the TLL and Ap showed a linear relationship. The effective excluded volume （EEV） of the PEG was obtained and it was found that EEV also increased as the pH increased.展开更多
Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approache...Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.展开更多
Unconventional formations are characterized by high heterogeneity and anisotropy,making it difficult to interpret logging data,perform core analysis,and create accurate petrophysical models.To address this challenge,a...Unconventional formations are characterized by high heterogeneity and anisotropy,making it difficult to interpret logging data,perform core analysis,and create accurate petrophysical models.To address this challenge,a new methodologyhas been developed that uses Bayesian networks to perform rock-typing in unconventional reservoirs.The research combines,for the first time,logging data and thermal core profiling data to enhance classification accuracy based on Bayesian theory.The material used in this study is unique,encompassing extended logging and core data,a large number of full-size core samples,and various Bayesian networks.Constrained-based,score-based,and hybrid algorithms were applied to identify network structures.Comparing different Bayesian networks'performance was made possible by applying the methodology to rock-typing of 2923 full-size samples taken from three wells drilled in the central part of West Siberia through the Bazhenov Formation.Hybrid algorithms-K2-MPC and HCMwsT-gives the networks providing the most successful rock-typing(up to 84%of correctly classified samples).Naive Bayes'rock-typing quality is the worst,which indicates that the input data is not independent.Accounting for some thermal core profiling data improves(up to 6%)the quality of the rocktyping.Lessons we learned in obtaining an effective network structure are described.Bayesian networks and Random Forest provide similar rock typing results,but Bayesian networks give probabilities for samples belonging to each of the rock types studied and also allows specialists to grasp the causal relationships among input parameters.This research offers a new perspective for the analysis of unconventional reservoirs.展开更多
Traditional formation pressure prediction methods all are based on the formation undercompaction mechanism and the prediction results are obviously low when predicting abnormally high pressure caused by compressional ...Traditional formation pressure prediction methods all are based on the formation undercompaction mechanism and the prediction results are obviously low when predicting abnormally high pressure caused by compressional structure overpressure.To eliminate this problem,we propose a new formation pressure prediction method considering compressional structure overpressure as the dominant factor causing abnormally high pressure.First,we establish a model for predicting maximum principal stress,this virtual maximum principal stress is calculated by a double stress field analysis.Then we predict the formation pressure by fitting the maximum principal stress with formation pressure. The real maximum principal stress can be determined by caculating the sum of the virtual maximum principal stresses.Practical application to real data from the A1 and A2 wells in the A gas field shows that this new method has higher accuracy than the traditional equivalent depth method.展开更多
基金Supported by the PetroChina Science and Technology Project(2023ZZ0202)。
文摘The formation water sample in oil and gas fields may be polluted in processes of testing, trial production, collection, storage, transportation and analysis, making the properties of formation water not be reflected truly. This paper discusses identification methods and the data credibility evaluation method for formation water in oil and gas fields of petroliferous basins within China. The results of the study show that: (1) the identification methods of formation water include the basic methods of single factors such as physical characteristics, water composition characteristics, water type characteristics, and characteristic coefficients, as well as the comprehensive evaluation method of data credibility proposed on this basis, which mainly relies on the correlation analysis sodium chloride coefficient and desulfurization coefficient and combines geological background evaluation;(2) The basic identifying methods for formation water enable the preliminary identification of hydrochemical data and the preliminary screening of data on site, the proposed comprehensive method realizes the evaluation by classifying the CaCl2-type water into types A-I to A-VI and the NaHCO3-type water into types B-I to B-IV, so that researchers can make in-depth evaluation on the credibility of hydrochemical data and analysis of influencing factors;(3) When the basic methods are used to identify the formation water, the formation water containing anions such as CO_(3)^(2-), OH- and NO_(3)^(-), or the formation water with the sodium chloride coefficient and desulphurization coefficient not matching the geological setting, are all invaded with surface water or polluted by working fluid;(4) When the comprehensive method is used, the data credibility of A-I, A-II, B-I and B-II formation water can be evaluated effectively and accurately only if the geological setting analysis in respect of the factors such as formation environment, sampling conditions, condensate water, acid fluid, leaching of ancient weathering crust, and ancient atmospheric fresh water, is combined, although such formation water is believed with high credibility.
基金supported by the National Natural Science Foundation of China(Grant numbers:52174012,52394250,52394255,52234002,U22B20126,51804322).
文摘Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm^(3),with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications.
基金This paper is financially supported by the National I mportant MiningZone Database ( No .200210000004)Prediction and Assessment ofMineral Resources and Social Service (No .1212010331402) .
文摘Geo-data is a foundation for the prediction and assessment of ore resources, so managing and making full use of those data, including geography database, geology database, mineral deposits database, aeromagnetics database, gravity database, geochemistry database and remote sensing database, is very significant. We developed national important mining zone database （NIMZDB） to manage 14 national important mining zone databases to support a new round prediction of ore deposit. We found that attention should be paid to the following issues： ① data accuracy： integrity, logic consistency, attribute, spatial and time accuracy; ② management of both attribute and spatial data in the same system;③ transforming data between MapGIS and ArcGIS; ④ data sharing and security; ⑤ data searches that can query both attribute and spatial data. Accuracy of input data is guaranteed and the search, analysis and translation of data between MapGIS and ArcGIS has been made convenient via the development of a checking data module and a managing data module based on MapGIS and ArcGIS. Using AreSDE, we based data sharing on a client/server system, and attribute and spatial data are also managed in the same system.
基金Supported by the CNPC Science and Technology Projects(2022-N/G-47808,2023-N/G-67014)RIPED International Cooperation Project(19HTY5000008).
文摘To solve the problems in restoring sedimentary facies and predicting reservoirs in loose gas-bearing sediment,based on seismic sedimentologic analysis of the first 9-component S-wave 3D seismic dataset of China,a fourth-order isochronous stratigraphic framework was set up and then sedimentary facies and reservoirs in the Pleistocene Qigequan Formation in Taidong area of Qaidam Basin were studied by seismic geomorphology and seismic lithology.The study method and thought are as following.Firstly,techniques of phase rotation,frequency decomposition and fusion,and stratal slicing were applied to the 9-component S-wave seismic data to restore sedimentary facies of major marker beds based on sedimentary models reflected by satellite images.Then,techniques of seismic attribute extraction,principal component analysis,and random fitting were applied to calculate the reservoir thickness and physical parameters of a key sandbody,and the results are satisfactory and confirmed by blind testing wells.Study results reveal that the dominant sedimentary facies in the Qigequan Formation within the study area are delta front and shallow lake.The RGB fused slices indicate that there are two cycles with three sets of underwater distributary channel systems in one period.Among them,sandstones in the distributary channels of middle-low Qigequan Formation are thick and broad with superior physical properties,which are favorable reservoirs.The reservoir permeability is also affected by diagenesis.Distributary channel sandstone reservoirs extend further to the west of Sebei-1 gas field,which provides a basis to expand exploration to the western peripheral area.
基金supported by National Natural Science Foundation of China (No.10475079)
文摘A new possible data archive format for storing huge amounts of data for EAST control anddata acquisition system is presented. This new general-purpose data archive format is network-transparent, i.e, machine-independent and has been implemented in terms of XDR （eXternal Data Representation）. We test this format by using EFIT （Equilibrium Fitting） code on different operation systems, namely Linux and Windows, different processors, namely Sun and Pc, and different programs, namely in Fortran and C language. It can be easily used by different computers and different programming languages.
文摘A typical building project has a long life in the maintenance stage. Also, the cost at this stage is enormously huge compared to planning, design and construction phases. In the earlier stage, which is planning or design phase, however, many project participants put little emphasis on the maintenance information. As a result, important maintenance data is missing and erroneously feedback to the 3D/BIM model. This research provides a generic process model for maintenance information management for building facilities. The authors have identified that there exist most-frequently used information areas: checking information, material information, equipment information, supplier information, and maintenance history information. Each information area should be embedded in the BIM model in order to effectively feedback to the operation and maintenance stage in the project. Thus, the study has proposed a novel data format structure which can effectively link the 3D/BIM object with the maintenance data. The demonstration project shows how the data format structure is used. The contribution of this study is to provide guidance to a project practitioner by step-by-step approach in dealing with the significant maintenance information in the earlier stage of the construction project.
文摘New liquid-liquid equilibrium data for polyethylene glycol （PEG） 3000 ＋ CHO2K ＋ H20 systems were measured at 298.15 K and pH values of 7.95, 8.40 and 9.98. It was found that an increase in pH caused the binodal curve to be displaced downward and the two-phase region to expand. Accordingly, the binodal curve was adjusted to the Pirdashti equation and the tie-line compositions were correlated using the Othmer-Tobias, Bancroft and Hand equations. The study measured the refractive index and densities of several homogeneous binary and ternary solutions. The solutions were used for calibration within a range of 0% to 30% of the mass of the PEG and potassium formate. The density and refractive index data show a linear variation with the mass fraction of the polymer and the salt. The effect of pH on the binodal, tie-line lengths （TLL） and slope of the tie-line （STL） in the systems was exam- ined. It was found that an increase in pH increased the TLL and decreased the STL It was observed that the density of the aqueous two-phase system was influenced by the TLL The difference in density between phases （△p） increased as the TLL and pH increased. It was found that the TLL and Ap showed a linear relationship. The effective excluded volume （EEV） of the PEG was obtained and it was found that EEV also increased as the pH increased.
基金funded by the Basic Science Centre Project of the National Natural Science Foundation of China(Grant No.72088101)supported by the Higher Education Commission,Pakistan(Grant No.20-14925/NRPU/R&D/HEC/2021-2021)+1 种基金the Researchers Supporting Project Number(Grant No.RSP2025R351)King Saud University,Riyadh,Saudi Arabia,for funding this research article.
文摘Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R^(2) score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation.
基金supported by the Ministry of Science and Higher Education of the Russian Federation provided under agreement No.075-10-2022-011 within the framework of the development program for a world-class Research Centre.
文摘Unconventional formations are characterized by high heterogeneity and anisotropy,making it difficult to interpret logging data,perform core analysis,and create accurate petrophysical models.To address this challenge,a new methodologyhas been developed that uses Bayesian networks to perform rock-typing in unconventional reservoirs.The research combines,for the first time,logging data and thermal core profiling data to enhance classification accuracy based on Bayesian theory.The material used in this study is unique,encompassing extended logging and core data,a large number of full-size core samples,and various Bayesian networks.Constrained-based,score-based,and hybrid algorithms were applied to identify network structures.Comparing different Bayesian networks'performance was made possible by applying the methodology to rock-typing of 2923 full-size samples taken from three wells drilled in the central part of West Siberia through the Bazhenov Formation.Hybrid algorithms-K2-MPC and HCMwsT-gives the networks providing the most successful rock-typing(up to 84%of correctly classified samples).Naive Bayes'rock-typing quality is the worst,which indicates that the input data is not independent.Accounting for some thermal core profiling data improves(up to 6%)the quality of the rocktyping.Lessons we learned in obtaining an effective network structure are described.Bayesian networks and Random Forest provide similar rock typing results,but Bayesian networks give probabilities for samples belonging to each of the rock types studied and also allows specialists to grasp the causal relationships among input parameters.This research offers a new perspective for the analysis of unconventional reservoirs.
基金a grant from the National Key Technologies R & D Program of China during the 9th Five-Year Plan Period(Grant No.9911010102).
文摘Traditional formation pressure prediction methods all are based on the formation undercompaction mechanism and the prediction results are obviously low when predicting abnormally high pressure caused by compressional structure overpressure.To eliminate this problem,we propose a new formation pressure prediction method considering compressional structure overpressure as the dominant factor causing abnormally high pressure.First,we establish a model for predicting maximum principal stress,this virtual maximum principal stress is calculated by a double stress field analysis.Then we predict the formation pressure by fitting the maximum principal stress with formation pressure. The real maximum principal stress can be determined by caculating the sum of the virtual maximum principal stresses.Practical application to real data from the A1 and A2 wells in the A gas field shows that this new method has higher accuracy than the traditional equivalent depth method.
