Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating d...Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.展开更多
Based on the well-logging data of typical wells of Zhijin,Panxian and Weining areas in western Guizhou,the well-logging data GR of late Permian coal-bearing strata were processed and wavelet transform technique was us...Based on the well-logging data of typical wells of Zhijin,Panxian and Weining areas in western Guizhou,the well-logging data GR of late Permian coal-bearing strata were processed and wavelet transform technique was used to carry out the sequence stratigraphy division and correlation.The study mainly focuses on the controlling effects which Milankovitch had on high frequency sequence,Milankovitch cycle can be used as a ruler of sequence stratigraphy division and correlation to ensure the scientifcity and the unity of sequence stratigraphy division.According to well-logging signal of the ideal Milankovitch cycle,the corresponding relation between the wavelet scales and the cycles is determined by wavelet analysis.Through analyzing analog signals of subsequence sets to search the corresponding relation between various system tracts and the features of time-frequency,the internal features of wavelet transform scalogram could be made clearly.According to ideal model research,features of Milankovitch curves and wavelet spectrum can be seen clearly and each well can be classifed into four third-order sequences and two system tracts.At the same time Milankovitch cycle can realize the division and correlation of stratigraphic sequence in a quick and convenient way.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
Two small scale acoustic phased arrays with 4 elements have been designed and assembled in the laboratory. Experiments have been carried out with them. It is found that both directivity and radiation lobe width of the...Two small scale acoustic phased arrays with 4 elements have been designed and assembled in the laboratory. Experiments have been carried out with them. It is found that both directivity and radiation lobe width of the phased array can be regulated by changing the time delay between the input signals on neighboring elements. Results measured are in good agreement with those calculated. By using the phased array as an acoustic transmitter and hydrophone as a receiver, small scale acoustic well-logging simulations have been carried out both on an aluminum modei well and on a concrete one. Experimental results show that, by increasing the time delay of the input signals on neighboring elements, the steered radiation angle of the phased array becomes larger and larger, and generation conditions of the refracted compressional wave and the refracted shear wave are reached successively, and the refracted compressional wave, the refracted shear wave and the Stoneley wave are strengthened, respec-tively. Therefore, by choosing element spacing of a phased array and acoustic wave frequency appropriately, the main radiation lobe of the phased array can be widened to cover the first critical angle of all kinds of formations, which makes it possible to apply phased array acoustic well-logging in any formation continuously without regulating directivity of the phased array.展开更多
Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon(TOC)in shales and tight formations.This is of value where only limited suites of...Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon(TOC)in shales and tight formations.This is of value where only limited suites of well logs are recorded,and few laboratory measurements of TOC are conducted on rock samples.Data from two Lower-Barnett-Shale(LBS)wells(USA),including well logs and core analysis is considered.It demonstrates how well-log attributes can be exploited with machine learning(ML)to generate accurate TOC predictions.Six attributes are calculated for gamma-ray(GR),bulk-density(PB)and compressional-sonic(DT)logs.Used in combination with just one of those recorded logs,those attributes deliver more accurate TOC predictions with ML models than using all three recorded logs.When used in combination with two or three of the recorded logs,the attributes generate TOC prediction accuracy comparable with ML models using five recorded well logs.Multi-K-fold-cross-validation analysis reveals that the K-nearest-neighbour algorithm yields the most accurate TOC predictions for the LBS dataset.The extreme-gradient-boosting(XGB)algorithm also performs well.XGB is able to provide information about the relative importance of each well-log attribute used as an input variable.This facilitates feature selection making it possible to reduce the number of attributes required to generate accurate TOC predictions from just two or three recorded well logs.展开更多
In petroleum exploitation, the main aim of resistivity well-logging is to determine the resistivity of the layers by measuring the potential on the electrodes. This mathematical problem can be described as an inverse ...In petroleum exploitation, the main aim of resistivity well-logging is to determine the resistivity of the layers by measuring the potential on the electrodes. This mathematical problem can be described as an inverse problem for the elliptic equivalued surface boundary value problem. In this paper, the author gets the expression of the derivative functions of the potential on the electrodes with respect to the resistivity of the layers. This allows us to solve the identification problem of the resistivity of the layers.展开更多
基金This paper is supported by the Engineering Center of Chinese Continental Scientific Drilling (No. CCSD2004-04-01)the Focused Subject Program of Beijing (No. XK104910598).
文摘Eclogite, one of the important lithologies in the main hole of the Chinese Continental Scientific Drilling （CCSD） Project, exists above the depth of 3 245 m and has distinctive responses of gamma-ray, compensating density and neutron well-logging, and so on. In this study, according to the diversities of minerals and chemical components and well-logging responses, edogites are classified from three aspects of origin, content of oxygen, and sub-mineral. We studied the logging identification method for eclogite sub-classes based on multi-element statistics and reconstructed 11 kinds of eclogite. As a result, eclogites can be divided into 6 types using well logs. In the light of this recognition, the eclogite in the main hole is divided into 20 sections, and the distribution characters of all sub-classes of eclogite are analyzed, which will provide important data for geological research of CCSD.
基金supported by the National Natural Science Foundation of China (No. 41072076)the Youth Foundation of the National Natural Science Foundation of China (No. 41102100)
文摘Based on the well-logging data of typical wells of Zhijin,Panxian and Weining areas in western Guizhou,the well-logging data GR of late Permian coal-bearing strata were processed and wavelet transform technique was used to carry out the sequence stratigraphy division and correlation.The study mainly focuses on the controlling effects which Milankovitch had on high frequency sequence,Milankovitch cycle can be used as a ruler of sequence stratigraphy division and correlation to ensure the scientifcity and the unity of sequence stratigraphy division.According to well-logging signal of the ideal Milankovitch cycle,the corresponding relation between the wavelet scales and the cycles is determined by wavelet analysis.Through analyzing analog signals of subsequence sets to search the corresponding relation between various system tracts and the features of time-frequency,the internal features of wavelet transform scalogram could be made clearly.According to ideal model research,features of Milankovitch curves and wavelet spectrum can be seen clearly and each well can be classifed into four third-order sequences and two system tracts.At the same time Milankovitch cycle can realize the division and correlation of stratigraphic sequence in a quick and convenient way.
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.
文摘Two small scale acoustic phased arrays with 4 elements have been designed and assembled in the laboratory. Experiments have been carried out with them. It is found that both directivity and radiation lobe width of the phased array can be regulated by changing the time delay between the input signals on neighboring elements. Results measured are in good agreement with those calculated. By using the phased array as an acoustic transmitter and hydrophone as a receiver, small scale acoustic well-logging simulations have been carried out both on an aluminum modei well and on a concrete one. Experimental results show that, by increasing the time delay of the input signals on neighboring elements, the steered radiation angle of the phased array becomes larger and larger, and generation conditions of the refracted compressional wave and the refracted shear wave are reached successively, and the refracted compressional wave, the refracted shear wave and the Stoneley wave are strengthened, respec-tively. Therefore, by choosing element spacing of a phased array and acoustic wave frequency appropriately, the main radiation lobe of the phased array can be widened to cover the first critical angle of all kinds of formations, which makes it possible to apply phased array acoustic well-logging in any formation continuously without regulating directivity of the phased array.
文摘Derivative/volatility well-log attributes from very few commonly recorded well logs can assist in the prediction of total organic carbon(TOC)in shales and tight formations.This is of value where only limited suites of well logs are recorded,and few laboratory measurements of TOC are conducted on rock samples.Data from two Lower-Barnett-Shale(LBS)wells(USA),including well logs and core analysis is considered.It demonstrates how well-log attributes can be exploited with machine learning(ML)to generate accurate TOC predictions.Six attributes are calculated for gamma-ray(GR),bulk-density(PB)and compressional-sonic(DT)logs.Used in combination with just one of those recorded logs,those attributes deliver more accurate TOC predictions with ML models than using all three recorded logs.When used in combination with two or three of the recorded logs,the attributes generate TOC prediction accuracy comparable with ML models using five recorded well logs.Multi-K-fold-cross-validation analysis reveals that the K-nearest-neighbour algorithm yields the most accurate TOC predictions for the LBS dataset.The extreme-gradient-boosting(XGB)algorithm also performs well.XGB is able to provide information about the relative importance of each well-log attribute used as an input variable.This facilitates feature selection making it possible to reduce the number of attributes required to generate accurate TOC predictions from just two or three recorded well logs.
文摘In petroleum exploitation, the main aim of resistivity well-logging is to determine the resistivity of the layers by measuring the potential on the electrodes. This mathematical problem can be described as an inverse problem for the elliptic equivalued surface boundary value problem. In this paper, the author gets the expression of the derivative functions of the potential on the electrodes with respect to the resistivity of the layers. This allows us to solve the identification problem of the resistivity of the layers.
