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
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
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
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
The Lower Indus Basin is the leading hydrocarbon-bearing sedimentary basin in Pakistan.This study has been conducted on the Sawan gas field located in the Lower Indus Basin,adjacent to a few other wellknown gas fields...The Lower Indus Basin is the leading hydrocarbon-bearing sedimentary basin in Pakistan.This study has been conducted on the Sawan gas field located in the Lower Indus Basin,adjacent to a few other wellknown gas fields of Pakistan like Kadanwari,Qadirpur,and Miano gas fields.This research aims to present the spatial distribution and reservoir potential of the productive zones of the Lower Goru Formation.The present study utilized various two-dimensional(2D)seismic lines and well-log data(Sawan-01 and Sawan-02)to investigate the structural and stratigraphic features of the area.The stratigraphic layers are mildly deepening in the southeast direction.The 2D seismic interpretation of the research area identifies the existence of extensional remanents,i.e.,normal faults.These extensional structures are associated with horst and graben geometry that acts as a trapping mechanism for hydrocarbons.Wireline logs are used to identify the reservoir's diverse lithology and petrophysical properties.Petrophysical results indicate fair to good effective porosities,low shale volume,and high hydrocarbon saturation(>55%),signifying good reservoir potential in C interval of the Lower Goru Formation.展开更多
Spontaneous potential well-logging is one of the important techniques in petroleum exploitation. A spontaneous potential satisfies an elliptic equivalued surface boundary value problem with discontinuous interface con...Spontaneous potential well-logging is one of the important techniques in petroleum exploitation. A spontaneous potential satisfies an elliptic equivalued surface boundary value problem with discontinuous interface conditions. In practice, the measuring electrode is so small that we can simplify the corresponding equivalued surface to a point. In this paper, we give a positive answer to this approximation process:when the equivalued surface shrinks to a point, the solution of the original equivalued surface boundary value problem converges to the solution of the corresponding limit boundary value problem.展开更多
The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, a...The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, and pore structure. We have developed a new method based on WT energy spectra analysis, by which the signal component reflecting the reservoir fluid property is extracted. We have successfully processed real log data from an oil field in central China using this method. The results of the reservoir fluid identification agree with the results of well tests.展开更多
The description of pores and fracture structures is a consistently important issue and certainly a difficult problem, especially for shale or tight rocks. However, the exploitation of so-called unconventional energy, ...The description of pores and fracture structures is a consistently important issue and certainly a difficult problem, especially for shale or tight rocks. However, the exploitation of so-called unconventional energy, such as shale methane and tight-oil, has become more and more dependent on an understanding of the inner structure of these unconventional reservoirs. The inner structure of porous rocks is very difficult to describe quantitatively using normal mathematics, but fractal geometry, which is a powerful mathematical tool for describing irregularly-shaped objects, can be applied to these rocks. To some degree, the cementation index and tortuosity can be used to describe the complexity of these structures. The cementation index can be acquired through electro-lithology experiments, but, until now, tortuosity could not be quantitatively depicted. This research used the well-logging curves of a gas shale formation to reflect the characteristics of the rock formations, and the changes in the curves to indicate the changes of the rock matrix, the pores, the connections among the pores, the permeability, and the fluid type. The curves that are affected most by the rock lithology, such as gamma ray, acoustic logging, and deep resistivity curves, can provide significant information about the micro-or nanostructure of the rocks. If the rock structures have fractal characteristics, the logging curves will also have fractal properties. Based on the definition of a fractal dimension and the Hausdorff dimension, this paper presents a new methodology for calculating the fractal dimensions of logging curves. This paper also reveals the deep meaning of the rock cementation index, m, through the Hausdorff dimension, and provides a new equation to calculate this parameter through the resistivity and porosity of the formation. Although it represents a very important relationship between the saturation of hydrocarbons with pores and resistivity, the Archie formula was not available for shale and tight rock. The major reason for this was an incorrect understanding of the cementation index, and the calculation of saturation used a single m value from the bottom to the top of the well. Unfortunately, this processing method is clearly inappropriate for the intensely heterogeneous material that is shale and tight rock. This paper proposes a method of calculating m through well-logging curves based on a fractal geometry that can change with different lithologies, so that it would have more agreement with in situ scenarios than traditional methods.展开更多
This work is devoted to the analysis of the formation conditions and geologic model of Paleozoic basement rocks of a number of oil-and-gas fields, located in Tomsk region(South of West-Siberian Oil-and-Gas Province,Ru...This work is devoted to the analysis of the formation conditions and geologic model of Paleozoic basement rocks of a number of oil-and-gas fields, located in Tomsk region(South of West-Siberian Oil-and-Gas Province,Russia).The research is based on integrated data interpretation of seismic exploration, well logging and deep drilling.The study is at the interfaces between exploration geophysics展开更多
Near 50 years history of the development of radioisotope neutron sources in China is briefly reviewed.The structure design,preparing technology and production status of routine neutron sources including 210Po-Be sourc...Near 50 years history of the development of radioisotope neutron sources in China is briefly reviewed.The structure design,preparing technology and production status of routine neutron sources including 210Po-Be sources,210Po mock fission sources,241Am-Be sources,238Pu-Be sources,252Cf spontaneous fission sources and other special-shape neutron sources are summarized.In addition,the prospects of development on radioisotope neutron source in China are predicted from the needs of nuclear power construction,oil well-logging,neutron moisture gauge and neutron brachytherapy.展开更多
A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion proces...A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-determined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduction of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petrophysical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of reservoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layerboundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example.展开更多
Conventional loggings provide the essential data for AVO （Amplitude-Versus- Offset） analysis in rock physics, which can build a bridge linking petrophysics and seismic data. However, if some complex fluid systems, s...Conventional loggings provide the essential data for AVO （Amplitude-Versus- Offset） analysis in rock physics, which can build a bridge linking petrophysics and seismic data. However, if some complex fluid systems, such as serious fluid invasion to formation, low resistivity response or complicated water salinity etc. exist in reservoirs, the conventional logs may fail to provide quality data, leading to calculated errors for elastic properties so worse that the AVO results cannot match the seismic data. To overcome such difficulties in Tertiary reservoirs of Bohai Gulf in China, we utilized both conventional logs and CMR- MDT tool （Combinable Magnetic Resonance and Modular Formation Dynamics Tester） to perform formation evaluation and reservoir descriptions. Our research proposes, it allows petrophysicists to acquire reservoir parameters （e.g. porosity, permeability, water saturation, bound fluids and pore pressure etc）, and then these results to combine with core analysis based on laboratory＇s measurements to carry out a further rock physics study and AVO analysis in seismic domain.展开更多
Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and d...Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and direct down-hole pressure measurements.However,a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many wells.Applying machine learning(ML)algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is limited.In this research,several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field,New Zealand.Their predictions substantially outperform,in terms of prediction performance,those generated using a multiple linear regression(MLR)model.The geophysical logs used as input variables are sonic,temperature and density logs,and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions.A total of 25,935 data records involving six well-log input variables were evaluated across the four wells.All ML methods achieved credible levels of pore pressure prediction performance.The most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree(DT),adaboost(ADA),random forest(RF)and transparent open box(TOB).The DT achieved root mean square error(RMSE)ranging from 0.25 psi to 14.71 psi for the four wells.The trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other wellbores.For two wells(Mangahewa-03 and Mangahewa-06),semi-supervised prediction achieved acceptable prediction performance of RMSE of 130—140 psi;while for the other wells,semi-supervised prediction performance was reduced to RMSE>300 psi.The results suggest that these models can be used to predict pore pressure in nearby locations,i.e.similar geology at corresponding depths within a field,but they become less reliable as the step-out distance increases and geological conditions change significantly.In comparison to other approaches to predict pore pressures,this study has identified that application of several ML algorithms involving a large number of data records can lead to more accurate prediction results.展开更多
There are few 3-D seismic profiles and drillings in the middle part of the Qibei depression in the Dagang oilfield, and more than 70% of the 2-D seismic profiles were completed before the 1980s. Meanwhile, changes in ...There are few 3-D seismic profiles and drillings in the middle part of the Qibei depression in the Dagang oilfield, and more than 70% of the 2-D seismic profiles were completed before the 1980s. Meanwhile, changes in the terrestrial formations in this region have been large and complex. These factors have made it difficult to predict reservoirs in this area. The purpose of this paper is to establish a methodology for predicting potential gas and oil reservoirs. Our research combines sequence stratigraphy, well-logs, and seismic analysis to elucidate the prediction of flagstone reservoirs in the S1 (Sha-I) Member in the middle of the Qibei depression. Previous research indicates that these rocks were deposited in an environment that had a semiarid, northern subtropical, and warm, humid climate. The objective strata currently consist mainly of lake fades, deeper lake facies, and shore-shallow lake facies. The study reveals that the lower section of the S1 Member is an important objective region for exploration.展开更多
The exploitation of a thin-layered reservoir with thickness less than 5 m(16 ft)needs to rely upon sedimentary facies analysis,due to the low resolution of seismic data and sparse well data.However,the ambiguity of fa...The exploitation of a thin-layered reservoir with thickness less than 5 m(16 ft)needs to rely upon sedimentary facies analysis,due to the low resolution of seismic data and sparse well data.However,the ambiguity of facies interpretation creates uncertainty in facies and reservoir mapping.The study aims to better define facies distribution,predict sand-body architecture,and consequently reduce risks in planning development wells,by integrating production data(dynamic)with conceptual geological models(static).An example is shown from the M1 Member of the Napo Formation in the B and G Oilfields(Block J),Oriente Basin,Ecuador.The M1 reservoir is a thin-layered sandstone reservoir below seismic resolution.Core and well-log facies interpretation indicates depositional facies of a tidedominated estuary.Production data during the water injection phase of field development can delineate the detailed geometry of the sand bodies.The injector-producer rate responses can determine the connectivity of sand bodies.Well performance during primary recovery can capture the scale of single reservoirs because the initial production rates and the cumulative oil production of individual wells are generally proportional to the size of sand bodies.A facies model was thereby generated.Two step-out wells were planned using this model and drilled successfully.The results demonstrate that the integration of dynamic and static data is critical to understanding reservoir facies distribution.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
Derivative and volatility attributes can be usefully calculated from recorded gamma ray(GR)data to enhance lithofacies classification in wellbores penetrating multiple lithologies.Such attributes extract information a...Derivative and volatility attributes can be usefully calculated from recorded gamma ray(GR)data to enhance lithofacies classification in wellbores penetrating multiple lithologies.Such attributes extract information about the log curve shape that cannot be readily discerned from the recorded well log data.A logged wellbore section for which 8911 data records are available for the three recorded logs(GR,sonic(DT)and bulk density(PB))is evaluated.That section demonstrates the value of the GR attributes for machine learning(ML)lithofacies predictions.Five feature selection configurations are considered.The 9-var configuration including GR,DT,PB and six GR attributes,and the 7-var configuration of GR and the six GR attributes,provide the most accurate and reproducible lithofacies predictions.The other three feature configurations evaluated do not include the GR attributes but just one to three of the recorded log features.The results of seven ML models and two regression models reveal that K-nearest neighbor(KNN),random forest(RF)and extreme gradient boosting(XGB)are the best performing models.They generate between 14 and 23 misclassification from 8911 data records for the 9-var model.Multi-layer perceptron(MLP)and support vector classification(SVC)do not perform well with the 7-var model which lacks the PB feature displaying the highest correlation with facies class.Annotated confusion matrices reveal that KNN,RF and XGB models can effectively distinguish all facies classes for the 9-var and 7-var configurations(that includes the GR attributes),whereas none of the models can achieve that outcome for the 3-var configuration(that excludes the GR attributes).Accurately distinguishing lithofacies using well-log data in sedimentary sections is an important objective in applied geoscience.The straightforward,GR-attribute method proposed works to improve confidence in ML-lithofacies classifications based on limited recorded well-log data.展开更多
Machine learning(ML)to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields.Meandering,braided fluviatile deposition...Machine learning(ML)to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields.Meandering,braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels.Three cored wellbores drilled through such a reservoir in a large oil field,with just four recorded well logs available,are used to classify four lithofacies using ML models.To augment the well-log data,six derivative and volatility attributes were calculated from the recorded gamma ray and density logs,providing sixteen log features for the ML models to select from.A novel,multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation.Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation.When the trained ML models were applied to a third well for testing,lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features.However,an accuracy of~0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well.A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with~0.6 accuracy.Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.展开更多
Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly ...Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.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.
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.
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The Lower Indus Basin is the leading hydrocarbon-bearing sedimentary basin in Pakistan.This study has been conducted on the Sawan gas field located in the Lower Indus Basin,adjacent to a few other wellknown gas fields of Pakistan like Kadanwari,Qadirpur,and Miano gas fields.This research aims to present the spatial distribution and reservoir potential of the productive zones of the Lower Goru Formation.The present study utilized various two-dimensional(2D)seismic lines and well-log data(Sawan-01 and Sawan-02)to investigate the structural and stratigraphic features of the area.The stratigraphic layers are mildly deepening in the southeast direction.The 2D seismic interpretation of the research area identifies the existence of extensional remanents,i.e.,normal faults.These extensional structures are associated with horst and graben geometry that acts as a trapping mechanism for hydrocarbons.Wireline logs are used to identify the reservoir's diverse lithology and petrophysical properties.Petrophysical results indicate fair to good effective porosities,low shale volume,and high hydrocarbon saturation(>55%),signifying good reservoir potential in C interval of the Lower Goru Formation.
文摘Spontaneous potential well-logging is one of the important techniques in petroleum exploitation. A spontaneous potential satisfies an elliptic equivalued surface boundary value problem with discontinuous interface conditions. In practice, the measuring electrode is so small that we can simplify the corresponding equivalued surface to a point. In this paper, we give a positive answer to this approximation process:when the equivalued surface shrinks to a point, the solution of the original equivalued surface boundary value problem converges to the solution of the corresponding limit boundary value problem.
基金This research is sponsored by Nation Natural Science Foundation of China （No.50404001 and No.50374048）.
文摘The wavelet transform （WT） method has been employed to decompose an original geophysical signal into a series of components containing different information about reservoir features such as pore fluids, lithology, and pore structure. We have developed a new method based on WT energy spectra analysis, by which the signal component reflecting the reservoir fluid property is extracted. We have successfully processed real log data from an oil field in central China using this method. The results of the reservoir fluid identification agree with the results of well tests.
基金the NSFC's(Natural Science Foundation of China.41974117)(National Science Project 20I7ZX05001004-005)Research on the Technology of Multilateral Fluid Derivation During Sand Production.
文摘The description of pores and fracture structures is a consistently important issue and certainly a difficult problem, especially for shale or tight rocks. However, the exploitation of so-called unconventional energy, such as shale methane and tight-oil, has become more and more dependent on an understanding of the inner structure of these unconventional reservoirs. The inner structure of porous rocks is very difficult to describe quantitatively using normal mathematics, but fractal geometry, which is a powerful mathematical tool for describing irregularly-shaped objects, can be applied to these rocks. To some degree, the cementation index and tortuosity can be used to describe the complexity of these structures. The cementation index can be acquired through electro-lithology experiments, but, until now, tortuosity could not be quantitatively depicted. This research used the well-logging curves of a gas shale formation to reflect the characteristics of the rock formations, and the changes in the curves to indicate the changes of the rock matrix, the pores, the connections among the pores, the permeability, and the fluid type. The curves that are affected most by the rock lithology, such as gamma ray, acoustic logging, and deep resistivity curves, can provide significant information about the micro-or nanostructure of the rocks. If the rock structures have fractal characteristics, the logging curves will also have fractal properties. Based on the definition of a fractal dimension and the Hausdorff dimension, this paper presents a new methodology for calculating the fractal dimensions of logging curves. This paper also reveals the deep meaning of the rock cementation index, m, through the Hausdorff dimension, and provides a new equation to calculate this parameter through the resistivity and porosity of the formation. Although it represents a very important relationship between the saturation of hydrocarbons with pores and resistivity, the Archie formula was not available for shale and tight rock. The major reason for this was an incorrect understanding of the cementation index, and the calculation of saturation used a single m value from the bottom to the top of the well. Unfortunately, this processing method is clearly inappropriate for the intensely heterogeneous material that is shale and tight rock. This paper proposes a method of calculating m through well-logging curves based on a fractal geometry that can change with different lithologies, so that it would have more agreement with in situ scenarios than traditional methods.
文摘This work is devoted to the analysis of the formation conditions and geologic model of Paleozoic basement rocks of a number of oil-and-gas fields, located in Tomsk region(South of West-Siberian Oil-and-Gas Province,Russia).The research is based on integrated data interpretation of seismic exploration, well logging and deep drilling.The study is at the interfaces between exploration geophysics
文摘Near 50 years history of the development of radioisotope neutron sources in China is briefly reviewed.The structure design,preparing technology and production status of routine neutron sources including 210Po-Be sources,210Po mock fission sources,241Am-Be sources,238Pu-Be sources,252Cf spontaneous fission sources and other special-shape neutron sources are summarized.In addition,the prospects of development on radioisotope neutron source in China are predicted from the needs of nuclear power construction,oil well-logging,neutron moisture gauge and neutron brachytherapy.
文摘A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-determined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduction of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petrophysical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of reservoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layerboundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example.
文摘Conventional loggings provide the essential data for AVO （Amplitude-Versus- Offset） analysis in rock physics, which can build a bridge linking petrophysics and seismic data. However, if some complex fluid systems, such as serious fluid invasion to formation, low resistivity response or complicated water salinity etc. exist in reservoirs, the conventional logs may fail to provide quality data, leading to calculated errors for elastic properties so worse that the AVO results cannot match the seismic data. To overcome such difficulties in Tertiary reservoirs of Bohai Gulf in China, we utilized both conventional logs and CMR- MDT tool （Combinable Magnetic Resonance and Modular Formation Dynamics Tester） to perform formation evaluation and reservoir descriptions. Our research proposes, it allows petrophysicists to acquire reservoir parameters （e.g. porosity, permeability, water saturation, bound fluids and pore pressure etc）, and then these results to combine with core analysis based on laboratory＇s measurements to carry out a further rock physics study and AVO analysis in seismic domain.
文摘Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and direct down-hole pressure measurements.However,a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many wells.Applying machine learning(ML)algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is limited.In this research,several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field,New Zealand.Their predictions substantially outperform,in terms of prediction performance,those generated using a multiple linear regression(MLR)model.The geophysical logs used as input variables are sonic,temperature and density logs,and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions.A total of 25,935 data records involving six well-log input variables were evaluated across the four wells.All ML methods achieved credible levels of pore pressure prediction performance.The most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree(DT),adaboost(ADA),random forest(RF)and transparent open box(TOB).The DT achieved root mean square error(RMSE)ranging from 0.25 psi to 14.71 psi for the four wells.The trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other wellbores.For two wells(Mangahewa-03 and Mangahewa-06),semi-supervised prediction achieved acceptable prediction performance of RMSE of 130—140 psi;while for the other wells,semi-supervised prediction performance was reduced to RMSE>300 psi.The results suggest that these models can be used to predict pore pressure in nearby locations,i.e.similar geology at corresponding depths within a field,but they become less reliable as the step-out distance increases and geological conditions change significantly.In comparison to other approaches to predict pore pressures,this study has identified that application of several ML algorithms involving a large number of data records can lead to more accurate prediction results.
文摘There are few 3-D seismic profiles and drillings in the middle part of the Qibei depression in the Dagang oilfield, and more than 70% of the 2-D seismic profiles were completed before the 1980s. Meanwhile, changes in the terrestrial formations in this region have been large and complex. These factors have made it difficult to predict reservoirs in this area. The purpose of this paper is to establish a methodology for predicting potential gas and oil reservoirs. Our research combines sequence stratigraphy, well-logs, and seismic analysis to elucidate the prediction of flagstone reservoirs in the S1 (Sha-I) Member in the middle of the Qibei depression. Previous research indicates that these rocks were deposited in an environment that had a semiarid, northern subtropical, and warm, humid climate. The objective strata currently consist mainly of lake fades, deeper lake facies, and shore-shallow lake facies. The study reveals that the lower section of the S1 Member is an important objective region for exploration.
基金the China National Science and Technology Major Project(No.2016ZX05002-006).
文摘The exploitation of a thin-layered reservoir with thickness less than 5 m(16 ft)needs to rely upon sedimentary facies analysis,due to the low resolution of seismic data and sparse well data.However,the ambiguity of facies interpretation creates uncertainty in facies and reservoir mapping.The study aims to better define facies distribution,predict sand-body architecture,and consequently reduce risks in planning development wells,by integrating production data(dynamic)with conceptual geological models(static).An example is shown from the M1 Member of the Napo Formation in the B and G Oilfields(Block J),Oriente Basin,Ecuador.The M1 reservoir is a thin-layered sandstone reservoir below seismic resolution.Core and well-log facies interpretation indicates depositional facies of a tidedominated estuary.Production data during the water injection phase of field development can delineate the detailed geometry of the sand bodies.The injector-producer rate responses can determine the connectivity of sand bodies.Well performance during primary recovery can capture the scale of single reservoirs because the initial production rates and the cumulative oil production of individual wells are generally proportional to the size of sand bodies.A facies model was thereby generated.Two step-out wells were planned using this model and drilled successfully.The results demonstrate that the integration of dynamic and static data is critical to understanding reservoir facies distribution.
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Derivative and volatility attributes can be usefully calculated from recorded gamma ray(GR)data to enhance lithofacies classification in wellbores penetrating multiple lithologies.Such attributes extract information about the log curve shape that cannot be readily discerned from the recorded well log data.A logged wellbore section for which 8911 data records are available for the three recorded logs(GR,sonic(DT)and bulk density(PB))is evaluated.That section demonstrates the value of the GR attributes for machine learning(ML)lithofacies predictions.Five feature selection configurations are considered.The 9-var configuration including GR,DT,PB and six GR attributes,and the 7-var configuration of GR and the six GR attributes,provide the most accurate and reproducible lithofacies predictions.The other three feature configurations evaluated do not include the GR attributes but just one to three of the recorded log features.The results of seven ML models and two regression models reveal that K-nearest neighbor(KNN),random forest(RF)and extreme gradient boosting(XGB)are the best performing models.They generate between 14 and 23 misclassification from 8911 data records for the 9-var model.Multi-layer perceptron(MLP)and support vector classification(SVC)do not perform well with the 7-var model which lacks the PB feature displaying the highest correlation with facies class.Annotated confusion matrices reveal that KNN,RF and XGB models can effectively distinguish all facies classes for the 9-var and 7-var configurations(that includes the GR attributes),whereas none of the models can achieve that outcome for the 3-var configuration(that excludes the GR attributes).Accurately distinguishing lithofacies using well-log data in sedimentary sections is an important objective in applied geoscience.The straightforward,GR-attribute method proposed works to improve confidence in ML-lithofacies classifications based on limited recorded well-log data.
文摘Machine learning(ML)to predict lithofacies from sparse suites of well-log data is difficult in laterally and vertically heterogeneous reservoir formations in oil and gas fields.Meandering,braided fluviatile depositional environments tend to form clastic sequences with laterally discontinuous layers due to the continuous shifting of relatively narrow sandstone channels.Three cored wellbores drilled through such a reservoir in a large oil field,with just four recorded well logs available,are used to classify four lithofacies using ML models.To augment the well-log data,six derivative and volatility attributes were calculated from the recorded gamma ray and density logs,providing sixteen log features for the ML models to select from.A novel,multiple-optimizer feature selection technique was developed to identify high-performing feature combinations with which seven ML models were used to predict lithofacies assisted by multi-k-fold cross validation.Feature combinations with just seven to nine selected log features achieved overall ML lithofacies accuracy of 0.87 for two wells used for training and validation.When the trained ML models were applied to a third well for testing,lithofacies ML prediction accuracy declined to 0.65 for the best performing extreme gradient boosting model with seven features.However,an accuracy of~0.76 was achieved by that model in predicting the presence of the pay bearing sandstone and siltstone lithofacies in the test well.A model using only the four recorded well logs was only able to predict the pay-bearing lithofacies with~0.6 accuracy.Annotated confusion matrices and feature importance analysis provide additional insight to ML model performance and identify the log attributes that are most influential in enhancing lithofacies prediction.
文摘Recently,machine learning(ML)has been considered a powerful technological element of different society areas.To transform the computer into a decision maker,several sophisticated methods and algorithms are constantly created and analyzed.In geophysics,both supervised and unsupervised ML methods have dramatically contributed to the development of seismic and well-log data interpretation.In well-logging,ML algorithms are well-suited for lithologic reconstruction problems,once there is no analytical expressions for computing well-log data produced by a particular rock unit.Additionally,supervised ML methods are strongly dependent on a accurate-labeled training data-set,which is not a simple task to achieve,due to data absences or corruption.Once an adequate supervision is performed,the classification outputs tend to be more accurate than unsupervised methods.This work presents a supervised version of a Self-Organizing Map,named as SSOM,to solve a lithologic reconstruction problem from well-log data.Firstly,we go for a more controlled problem and simulate well-log data directly from an interpreted geologic cross-section.We then define two specific training data-sets composed by density(RHOB),sonic(DT),spontaneous potential(SP)and gamma-ray(GR)logs,all simulated through a Gaussian distribution function per lithology.Once the training data-set is created,we simulate a particular pseudo-well,referred to as classification well,for defining controlled tests.First one comprises a training data-set with no labeled log data of the simulated fault zone.In the second test,we intentionally improve the training data-set with the fault.To bespeak the obtained results for each test,we analyze confusion matrices,logplots,accuracy and precision.Apart from very thin layer misclassifications,the SSOM provides reasonable lithologic reconstructions,especially when the improved training data-set is considered for supervision.The set of numerical experiments shows that our SSOM is extremely well-suited for a supervised lithologic reconstruction,especially to recover lithotypes that are weakly-sampled in the training log-data.On the other hand,some misclassifications are also observed when the cortex could not group the slightly different lithologies.
