Conventional Log Data Research Articles

The effects of planar structures on reservoir quality of Triassic Kangan formation in the central Persian Gulf, an integrated approach

Planar structures including stylolites, solution seams, dense streaks, bedding and cross-bedding surfaces, and fractures of carbonate Early Triassic Kangan Formation were evaluated in the central Persian Gulf. Core X-ray computed tomography (CT), Fullbore Formation Micro-Imager (FMI) log, and cores were used to detect the structures. Furthermore, conventional borehole logging data, routine core analysis, and petrographic thin sections were employed to assess the reservoir properties. Stereographic projections demonstrated that the reservoir has two sets of planar structures. The first group contains shallowly-dipping structures, consisting of stylolites, solution seams, dense streaks, bedding and cross-bedding surfaces. These structures have been created by sedimentary and diagenetic processes. The other set includes deformation-related structures that are steeply-dipping and occurred scarcely, like fractures. Spearman correlation coefficients between the frequency of each planar structure and reservoir characteristics (porosity and permeability) were calculated to reveal the numerical relationship between the parameters. The significant correlation coefficients indicate that stylolites, solution seams, and filled tension gashes diminish reservoir quality while bedding and cross-bedding surfaces have enhanced reservoir characteristics. The strongest correlation was observed as a strong negative relationship between the frequency of stylolites and permeability in K2 reservoir unit. This study implies that sedimentary and diagenetic factors are more effective in controlling the reservoir quality than deformation factors in the studied formation. The present work also indicates that each used method has unique advantages and disadvantages. Therefore, simultaneous utilization of the methods to investigate planar structures would have the best efficiency.

A practical approach to develop a proper anisotropic rock physics model for media comprising of multiple fracture sets in the absence of sophisticated laboratory/wireline data

For economical production from a fractured reservoir, a characteristic analysis of the fracture parameters like its density and orientation within the reservoir is essential to improve the fluid flow during extraction. This study deals with the development of a proper anisotropic rock physics model for a media with multiple fracture sets to study the spatial distribution of important fracture parameters i.e., fracture density and orientation in the absence of sophisticated laboratory/wireline and pre-stack seismic data. The crest of hydrocarbon producing fault-bounded Balkassar Anticline in Northern Potwar, Upper Indus Basin, Pakistan is selected as a case study representing a potential zone for development of fractures at reservoir level (Sakesar Limestone). The methodology consists of the interpretation of 3D post-stack seismic and conventional wireline log data to demarcate the reservoir containing fractures. The Ant-tracking discrete fracture network (DFN) attribute is applied on 3D post-stack seismic data to obtain an initial estimate about the presence of fracture corridors and their orientations. Based on this initial estimate, a proper rock physics model has been developed utilizing inverse Gassmann relations, T-matrix approximation, and Brown and Korringa relations. The output from the developed rock physics model has been displayed in the form of 13 effective independent elastic stiffness constants (monoclinic symmetry–representing media comprising of multiple fracture sets) as a function of fracture densities and azimuthal fracture orientations. A clear decreasing trend in effective elastic stiffness constants with increasing fracture densities can be observed. Similarly, a periodic trend of effective elastic stiffness constants with fracture orientations can be observed. These trends are more or less expected, but they would have been difficult to quantify without a proper rock physics model. The use of independent effective elastic constants for the generation of synthetic seismic amplitude versus angle and azimuth (AVAZ) data and its correlation with observed seismic AVAZ data in a geostatistical sense has been discussed.

Logging evaluation of deep multi-type unconventional gas reservoirs in the Songliao basin, northeast China: Implications from continental scientific drilling

Multi-type unconventional gas-bearing reservoirs with different lithologies and gas accumulation potential occur in the deep part of the Songliao basin. However, the reservoirs are non-homogeneous, the gas components differ substantially, and not all types of gas-bearing reservoirs have been identified or evaluated. The International Continental Scientific Drilling Program (ICDP) is used as an example to conduct qualitative and quantitative evaluations of deep multi-type unconventional gas-bearing reservoirs using conventional and specialized logging data. The core test data are used to determine the physical properties. The porosity and permeability are compared and analyzed using different methods and models. The results show that the reservoirs have low to ultra-low porosity and ultra-low permeability. Based on the comparison of the rock mechanical parameters and mineral composition, brittleness evaluation parameters are proposed for different types of deep reservoirs in the study region. The mineral brittleness index is highly consistent with the brittleness index based on rock mechanics. An identification method for deep multi-type gas-bearing reservoir and a classification approach for different gas properties are established based on the logging response and parameter interpretation. The methane gas reservoirs have low density (DEN) and low compensated neutron logging (CNL) values and high acoustic (AC) time difference and high resistivity (RT) values. The CO2 gas reservoirs have lower RT values and higher CNL values than the hydrocarbon gas reservoirs. The comprehensive analysis of deep gas source rock conditions and of the source-reservoir relationship provides insights for the evaluation of deep multi-type unconventional gas reservoirs. The discovery of high hydrogen content is of significant importance for developing new areas for deep natural gas exploration.

Feasibility of source-free DAS logging for next-generation borehole imaging

Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.

Petro-physics Analysis and Rock Physics Modeling for Estimation of Gas Hydrate Saturation: A Case Study in the Mahanadi Offshore Basin

Abstract Gas hydrate saturation (Sh) is the most challenging key parameter of petrophysical evaluations for reservoir characterization. In total three wells were used (namely, NGHP-01-19, NGHP-01-09, and NGHP-01-08) to compute the petrophysical parameters in the gas hydrate-bearing sediments of the Mahanadi offshore basin. Initially, effective porosity and volume of shale are computed using conventional log data varying from 11-36% and 55-75% respectively. The most prominent technique responding to the physical property has been used to compute the Sh by using Archie’s empirical electrical resistivity method. The presence of gas hydrate, free gas, and other lithology affects the sonic velocity which is widely used to quantify the hydrate saturation. Therefore,an attempt is made to quantify the Sh from the wellbore sonic velocity using a rock physics model at higher porosity (~62%) for unconsolidated marine sediments below the seafloor. The model is best suited for gas hydrate zones where velocity increases with the hydrate saturation but underestimates the saturation for free gas zones below the bottom simulating reflector. In the study area,Sh ranges from 5-13% in the depth interval of 175-200 mbsf for well NGHP-01-19 whereas, small amount of 3-10%and 2-8% for well NGHP-01-09 and NGHP-01-08 respectively. The Sh obtained from the rock physics model is insignificantly mismatched with the saturation obtained from temperature and resistivity data due to the presence of an isotropic layer with fracture filling sediments having anisotropic properties.

“Detection and characterization of fractures in the Eocene Thebes formation using conventional well logs in October field, Gulf of Suez, Egypt”

Fractures are one of the most prevalent and important geological features in petroleum exploration and production, as they have a substantial impact as conduits for hydrocarbon flow and improve the overall permeability of the formation. Despite their necessity, detecting and characterizing natural fractures still represents a difficult challenge. This study provides a technique for detecting and characterizing naturally fractured reservoirs using conventional well logs in the Eocene Thebes Formation, October Field, Gulf of Suez. Especially as this technique is not applied widely in October Field or even in the Gulf of Suez. Most carbonate reservoirs are complex and heterogeneous; one of the reasons is their naturally fractured characteristics. These fractures can significantly affect reservoir behavior, performance, and production. Despite being the most commonly available data source, logs are rarely employed in a systematic way to have a complete quantitative analysis of naturally fractured reservoirs. Since the presence of fractures affects all well logs in one way or another. This study presents an integrated workflow for determining fracture presence potentiality by combining conventional well logs, thin sections, and other available data in absence of directly advanced logging technologies such as Formation MicroImager (FMI), Dipole Shear Image (DSI), and Borehole Televiewer (BHTV). This integrated workflow was very effective and useful in the evaluation of potential fractures' existence, reservoir characterization, and development. Finally, the results of this integrated workflow suggest a high probability of fracture existence and identification in Thebes Formation, confirming that integration between conventional logging and other available data is very precious, and has a good potential to be used in absence of direct advanced methods for fractured reservoir characterization. For further studies, core data and advanced logs would be beneficial for correlation, since they would provide a more accurate picture of the fracture parameters.

Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.

Prediction of Shear Wave Velocity Based on a Hybrid Network of Two-Dimensional Convolutional Neural Network and Gated Recurrent Unit

Compressional and shear wave velocities (Vp and Vs, respectively) are important elastic parameters to predict reservoir parameters, such as lithology and hydrocarbons. Due to acquisition technologies and economy, the shear wave velocity is generally lacking. Over the last few years, some researchers proposed deep learning algorithms to predict the shear wave velocity using conventional logging data. However, these algorithms focus either on spatial feature extraction for different physical properties of rocks or on sequential feature extraction in the depth direction of rocks. Only focusing on feature extraction in a direction of rocks might lead to a decrease in prediction accuracy. Therefore, we propose a hybrid network of a two-dimensional convolutional neural network and the gated recurrent unit (2DCNN-GRU), which can establish more complex nonlinear relationships between the input and output data based on the spatial features extracted by 2DCNN and the sequential features extracted by GRU. In this study, two cases are used to validate the reliability and prediction accuracy of the proposed network. Comparing the prediction results of 2DCNN, GRU, and the proposed network, the proposed network shows better performance. Meanwhile, for improving the prediction accuracy of the proposed network, the relationship is analyzed between the prediction accuracy of the proposed network and the length of the input sample.

A Novel Method for Conventional Logging Prediction of Brittleness Index: A Case of Tight Sandstone in Western Ordos Basin

Brittleness index is an important parameter for the exploration and development of tight sandstone reservoir. In this paper, a new method for calculating brittleness index using conventional logging data was established to find the high-quality exploration and development target area of tight sandstone reservoir in He8 Member of Permian in the western margin of the Ordos Basin. Firstly, the mineral composition and content of rock samples in the study area were analyzed by rock mineral X-ray diffraction, and the brittleness index was calculated according to the mineral content with the average of 70.85%. Then, three kinds of brittleness index logging prediction models were obtained by conventional logging curves such as natural gamma and compressional wave interval transit time. Each prediction model was corrected by the brittleness index calculated according to the X-ray diffraction experiment, and the correlation coefficients were 0.83, 0.58, and 0.71, respectively. Finally, through multiple regression, the comprehensive equation for brittleness index prediction was established by combining these three kinds of models. According to the example of the specific well in the study area, the brittleness index predicted by conventional logging is highly matched with the interpretation results of array acoustic logging with the correlation coefficient of 0.90, which means the accuracy of the comprehensive equation has been verified. The new method can evaluate reservoir brittleness by conventional logging data in the absence of rock sample test data and popularize the application of brittleness index in oil and gas field exploration and development.

New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data

The maximum (Shmax) and minimum (Shmin) horizontal stresses are essential parameters for the well planning and hydraulic fracturing design. These stresses can be accurately measured using field tests such as the leak-off test, step-rate test, and so forth, or approximated using physics-based equations. These equations require measuring some in situ geomechanical parameters such as the static Poisson ratio and static elastic modulus via experimental tests on retrieved core samples. However, such measurements are not usually accessible for all drilled wells. In addition, the recently proposed machine learning (ML) models are based on expensive and destructive tests. Therefore, this study aims at developing a new approach to predict the least principal stresses in a time- and cost-effective way. New models have been developed using ML approaches, that is, artificial neural network (ANN) and support vector machine (SVM), to predict Shmin and Shmax gradients (outputs) from well-log data (inputs). A wide-ranged set of actual field data were collected and extensively analyzed before being fed to the algorithms to train the models. The developed ANN-based models outperformed the SVM-based ones with a mean absolute average error (MAPE) not exceeding 0.30% between the actual and predicted output values. Besides, new equations have been developed to mimic the processing of the optimized networks. The new empirical equations were verified by another unseen data set, resulting in a remarkably matched actual stress-gradient values, confirmed by a prediction accuracy exceeding 90% in addition to an MAPE of 0.43%. The results’ statistics confirmed the robustness of the developed equations to predict the Shmin and Shmax gradients with a high degree of accuracy whenever the logging data are available.

Convolutional neural network long short-term memory deep learning model for sonic well log generation for brittleness evaluation

Brittleness index, usually calculated by P- and S-wave velocities, is an important parameter used to optimize the sweet spots of shale oil. Empirical relationships or artificial intelligence networks can predict sonic logs based on conventional logging data, but accuracy is limited by the formation types and properties, such as shale sandstone interbeds. Therefore, we propose a hybrid convolutional neural network long short-term memory (CNN-LSTM) deep learning model for the prediction of compressional and shear traveltimes. The new model can extract nonlinear features as well as fluctuating trends of log response features with depth, which is different from most machine learning methods that only consider extracting spatial features between logs or only time-series features of log data sets. We conclude that the new CNN-LSTM network has the highest prediction accuracy (92% and 91.3%) and advantages in predicting curve mutation points compared with other machine learning models. We apply the predicted compressional and shear sonic times for evaluating brittleness to reduce the risk of exploration in shale oil reservoirs.

Fine Logging Evaluation of Fractures in Continental Tight Oil Sandstone Reservoirs of Yanchang Formation in the Ordos Basin

Fractures are the main seepage channels in tight reservoirs, and they affect the distribution of high-quality reservoirs and the enrichment of hydrocarbons. Fine logging identification of fractures in the strongly heterogeneous continental tight oil reservoirs of the Upper Triassic Yanchang Formation in the Ordos Basin is a hot and difficult point in the field of petroleum geology. In this paper, taking the Yanchang Formation as an example, a fractal model was constructed to identify fractures in tight oil reservoirs using a large number of cores, conventional and micro-resistivity imaging logging data. The results show that high-angle, vertical and bedding fractures are mainly developed in the Yanchang Formation tight sandstones. There is a negative correlation between sand body thickness and fracture development degree. The fracture sensitivity parameters were used to construct a coupled fractal fracture index. The fractal model incorporates logging information from natural gamma, acoustic wave time difference, rock density, and shallow lateral resistivity. In addition, the constructed fracture fractal index realizes the functions of multi-conventional logging information fusion, which can effectively identify fracture development segments in sandstone. According to the statistics, the fracture identification rate is 83.3%. The study also found that with the increase of sandstone brittleness index, the fracture index has a “S” shape increasing trend. Therefore, the content of brittle mineral components in sandstone is an important factor affecting the development degree of natural fractures, and fractures are more likely to occur in high brittle, thin sand bodies. The highly brittle framework minerals have strong stress-supporting capacity, which can keep fractures open by resisting high overlying loads.

Prediction of Water Saturation in Tight Gas Sandstone Formation Using Artificial Intelligence.

Water saturation (Sw) is a vital factor for the original oil and gas in place (OOIP and OGIP). Numerous available equations can be used to calculate Sw, but their values have been unreliable and strongly depend on core analyses, which are costly and time-consuming. Hence, this study implements artificial intelligence (AI) modules to predict Sw from the conventional well logs. Artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) were applied to estimate Sw using gamma-ray (GR) log, neutron porosity (NPHI) log, and resistivity (Rt) log. A data set of 782 points from two wells (Well-1 and Well-2) in tight gas sandstone formation was used to develop and test the different AI modules. Well-1 was used to construct the AI models, then the hidden data set from Well-2 was applied to validate the optimized models. The results showed that the ANN and ANFIS models were able to accurately estimate Sw from the conventional well logging data. The correlation coefficient (R) values between the actual and estimated Sw from the ANN model were found to be 0.93 and 0.91 compared to 0.95 and 0.90 for the ANFIS model during the training and testing processes. The average absolute percentage error (AAPE) was less than 5% in both models. A new empirical correlation was established using the biases and weights from the developed ANN model. The correlation was validated with the unseen data set from Well-2, and the correlation coefficient between the actual and the estimated Sw was 0.91 with an AAPE of 6%. This study provides AI application with an empirical correlation to estimate the water saturation from the readily available conventional logging data without the requirement for experimental analysis or well site interventions.

Prediction of Vitrinite Reflectance of Shale Oil Reservoirs Using Nuclear Magnetic Resonance and Conventional Log Data

Prediction of Vitrinite Reflectance of Shale Oil Reservoirs Using Nuclear Magnetic Resonance and Conventional Log Data

A novel hybrid method of lithology identification based on k-means++ algorithm and fuzzy decision tree

Lithology identification methods based on conventional logging data are essential in reservoir geological evaluation. Due to the highly non-linear relationship between lithology and various logging parameters, conventional methods cannot meet the requirements. In recent years, machine learning methods such as Neural Networks and decision tree have been applied to the field of lithology identification and achieved good effects. However, there is no obvious difference in logging parameters for various types of lithology, and at the same time, there is a large amount of information redundancy between each logging curve. Therefore, its uncertainty and fuzziness are high, which interferes with the result of lithology identification. Combining fuzzy theory, decision tree and K-means++ algorithm, this paper proposes a novel hybrid technique of lithology identification which can better overcome the ambiguity and uncertainty of logging data. In the actual data test, we select six logging parameters: density (RHOB), neutron porosity (NPHI), natural gamma (GR), longitudinal wave velocity (VP), shallow formation resistivity (LLS), and deep formation resistivity (LLD). Then K-means++ clustering algorithm was used for clustering analysis on logging data. Finally, the triangular membership function is selected to fuzz the logging data according to the obtained clustering center points, and a fuzzy decision tree lithology identification model is constructed. The prediction accuracy of the model reached 93.92%. The fuzzy decision tree algorithm was also compared with five machine learning algorithms, including decision tree, extremely randomized trees (ET), Adaboost, random forest (RF) and gradient boosting decision tree (GBDT). The results show that the modeling results of fuzzy decision tree algorithm outperform other algorithms. In summary, the fuzzy decision tree model developed in the study is a practical and effective model for complex lithology identification, providing a new idea for lithology identification. • A fuzzy decision tree model using logging data for lithology identification is proposed. • The k-means++ algorithm optimizes the selection of initial clustering centers. • Logging data is fuzzily processed by the triangular membership function. • Pre-pruning the fuzzy decision tree by controlling the values of significance level α and truth degree threshold β. • The accuracy of the model can reach 93.92%, which is better than other algorithms.

Identification of lithofacies and prediction of mineral composition in shales – A case study of the Shahejie Formation in the Bozhong Sag

The identification of shale lithofacies is the basic work of shale gas exploration and development. Accurate quantitative characterization of different mineral components in fine-grained mixed shale is of great significance for the identification and classification of lithofacies types, the enrichment conditions of shale oil and gas (hydrocarbon generation, reservoir, occurrence and preservation), and the evaluation of shale oil potential (reservoir, oil bearing, fracturability and oil mobility). When organic-rich argillaceous laminae are moderately mixed with brittle laminae, the laminated shale is not only the favorable interval of shale oil and gas enrichment for hydrocarbon generation (organic-rich argillaceous laminae) and storage (brittle laminae), but also the excellent “sweet spots” for continental shale oil and gas optimization. Different shale lithofacies have distinct rock texture, fabric and composition, leading to different brittleness and rock physical properties. For the identification of shale lithofacies containing various components, the overlaps of different wire-line logging responses and the vague boundaries between various logging data cause the large deviation in the logging prediction of lithofacies by traditional methods. In this study, shale samples from the lower part of the third member of Shahejie Formation in Bozhong Sag of the Bohai Bay Basin are selected to carry out the reservoir characterization and then the data mining of logging information by a Back Propagation (BP) neural network coupled with Atomic Search Optimization (ASO) algorithm. The BP algorithm based on the identified shale lithofacies (expected output) and logging data index (input) is used to train the neural network. The complex and unrecognized nonlinear relationship between shale lithofacies and logging data is mapped onto the high-dimensional identifiable nonlinear quantitative relationship to establish the prediction model of the relative content of clay minerals, silicate minerals and carbonate minerals. This study reveals the main lithologic characteristics of lacustrine shale lithofacies from Shahejie Formation in Bozhong Sag, and the main controlling factors for shale lithofacies prediction based on logging data. Our results show that the main mineral composition of shale lithofacies associations can be effectively predicted through the whole rock X-ray diffraction data, wire-line log data and neural network analysis, which provides the basis for lithofacies identification shale interval in well locations lacking core and test data. • Based on the optimized algorithm, a neural network prediction model of mineral composition is established to mine the hidden information in conventional logging data to predict lithofacies, and the accuracy is improved compared with the conventional neural network model. • The prediction of lithofacies mineral composition data for a wider range of non-coring wells by neural network provides more geological basis for the selection and prediction of favorable lithofacies zone - dessert area in subsequent exploration.

Reservoir Properties of the Upper Oligocene-Lower Miocene Ibrahim Formation in the Garmian Area, Iraqi Kurdistan Region

Reservoir characteristics of the Upper Oligocene-Lower Miocene Ibrahim Formation has been studied from the well Hasira-1 (H-1) at Sarqala Oilfield and from the well Taza-3 (Tz-3) at Taza Oilfield in Garmian Area/ southeast Kirkuk City in the Iraqi Kurdistan Region. The available conventional wireline log data are used for detecting the dominant lithology of the formation which appeared to be limestone and argillaceous limestone. The formation in the well H-1 contains appreciable quantity of shale which in some horizons exceeded 35%. The formation is of poor porosity (5% and 8% as average porosity in the well H-1 and Tz-3 respectively) and of poor to fair permeability with an average of 1.9mD in the well H-1. The formation divided to five reservoir units depending on variations in the shale content, porosity and permeability in the well H-1. RU-2 and RU-4 at depth intervals 4125-4128m and 4109-4114m respectively are of relatively the highest reservoir quality among the five recognized reservoir units in the formation. Light hydrocarbons are exist along the Ibrahim Formation in the well H-1 with no effective movability for to be recovered. The examined 17 core samples of the formation in the well Taza-3 is lithologically composed of argillaceous and marly limestone which represented by planktonic foraminifera bearing packstone microfacies. The average porosity of the tested core plug samples of the formation in the well Tz-3 is about 5%, so the formation in this well is of poor reservoir quality as it is in the well H-1.

A new method of mineral inversion based on error analysis and static response equation error: A case study of a shale gas reservoir in the Wufeng-Longmaxi Formation

The accurate quantitative calculation of mineral components is very important and basic work in formation evaluation. Using well-log data to estimate mineralogy and porosity is a mainstream method with core measurements often used. However, in shale reservoirs, there are many mineral components, such as organic matter and pyrite. In addition, the pore structure is complex, and gas exists in the pores as free state, adsorbed state, and dissolved state. These factors make the logging response characteristics more complex and thus the estimation of the mineral components more difficult. To address this problem, we have adopted a mineral inversion method based on error analysis and response equation error. Based on the error analysis of the mineral inversion method, we first establish a technique to obtain interpretation parameters and the function of the response equation error combined with the core data. Then, based on the weighted total least-squares method, we construct the objective function, and we use the improved krill herd algorithm to solve the problem. Finally, we estimate the mineral component volume. The calculated results indicate that the method can accurately determine the clay, quartz + feldspar, carbonate contents, and porosity by conventional logging data. Compared to the traditional mineral inversion method, the average relative error is reduced by 11.1%. The method has high applicability to shale reservoirs and can supply the basic parameters for formation evaluation.

The application of machine learning under supervision in identification of shale lamina combination types — A case study of Chang 73 sub-member organic-rich shales in the Triassic Yanchang Formation, Ordos Basin, NW China

Organic rich laminated shale is one type of favorable reservoirs for exploration and development of continental shale oil in China. However, with limited geological data, it is difficult to predict the spatial distribution of laminated shale with great vertical heterogeneity. To solve this problem, taking Chang 73 sub-member in Yanchang Formation of Ordos Basin as an example, an idea of predicting lamina combinations by combining 'conventional log data — mineral composition prediction — lamina combination type identification' has been worked out based on machine learning under supervision on the premise of adequate knowledge of characteristics of lamina mineral components. First, the main mineral components of the work area were figured out by analyzing core data, and the log data sensitive to changes of the mineral components was extracted; then machine learning was used to construct the mapping relationship between the two; based on the variations in mineral composition, the lamina combination types in typical wells of the research area were identified to verify the method. The results show the approach of 'conventional log data — mineral composition prediction — lamina combination type identification' works well in identifying the types of shale lamina combinations. The approach was applied to Chang 73 sub-member in Yanchang Formation of Ordos Basin to find out planar distribution characteristics of the laminae.

Application of Micro-resistivity Scanning Imaging Logging Data in Reservoir Thickness Division

Reservoir parameter interpretation is one of the main contents of reservoir description, which affects the whole process of oilfield development. According to the characteristics of micro-resistivity scanning imaging logging, which can directly reflect the changes of lithology and physical properties of reservoirs, this paper compares the thickness and interbed division of reservoirs with conventional logging data, this paper finds out the shortcomings of the conventional logging data in the interpretation of thickness and the division of interlayers, and combines the core analysis data to examine the differences in the correlation on the coring wells, and obtains good results, it has laid the foundation for the establishment of new interpretation procedure.