We analyze the sensitivity of a three-dimensional crosswell electromagnetic (EM) system based on finite-difference time-domain modeling. To reinforce the simulation accuracy of low-frequency electromagnetic propagation in a small space, we limit the boundary reflection energy by suppressing the numerical dispersion of convolutional perfectly matched layers. The numerical simulation results for an isotropic medium indicate that an anomalous conductivity will induce a large magnetic field strength disturbance at the receiving coils in the X- and Z-directions, and a relatively large absolute sensitivity is observed at the receiving coils in the Y-direction. The three-component signals are most sensitive to high-resistance anomalies. Through an experiment that involved moving an anomalous conductivity disturbance, comparison plots of dynamic residual hydrocarbon monitoring between wells and depth determinations of anomalous conductivities are provided. The optimal acquisition depth interval of the receiving coil in the homogeneous medium is given, and the effective exploration area and optimal modeling region of the crosswell EM system are obtained.

The present study aims to better understand the mineralogy and thermal structure of the Yingxiu-Beichuan fault zone (YBFZ), Sichuan basin, China, which was lacking previously. The previous research on the Wenchuan earthquake Fault Scientific Drilling (WFSD) project was focused on mineral classification, fault analysis, and geochemical research utilizing original logs using WFSD-1 well and at a shallow depth of 700 meters. No investigations are conducted at a depth of around 1550 meters utilizing multiple dimensionality reduced well-logs. Thereafter, we sought to categorize the minerals along the YBFZ using Machine Learning (ML) technologies and concentration-number (C-N) modeling using multiple WFSD-1 and WFSD-2 wells. In the categorization of rocks, three classifiers are discussed: Support Vector Machines (SVM), Feed Forward Back Propagation (BPNN), and Radial Basis Function Neural Networks (RBFN). Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to normalize the log data in a geologically complicated region. By using the BPNN classifier to PCA, LDA, and all 17 logs, we achieved respective accuracy rates of 75.54%, 86.32%, and 72.54%, which are higher than the accuracy rates of RBFN (76.03%, 77.01%, and 65.3%), and SVM (74.45%, 78.03%, and 70.06%). These results suggest that BPNN shows improved accuracy rates for mineral classification in a complex tectonic regime. In addition, concentration-number (C-N) fractal model technique and log-log plots are also used to characterize geothermal features and the results of C-N modeling supports the results of ML models. High gamma ray (GR) ranges of 349.7API, heat production (HP) ranges of 5.5 & mu;W/m3, and low thermal conductivity (TC) ranges of 0.28 W/km show that the fault region is associated with comparatively strong radiogenic activity compared to its surroundings.

The resistivity anisotropy characteristic of near-tight sandstone reservoirs is important for reservoir evaluation, but it is a challenge to obtain it by using conventional logging curves in vertical boreholes. Thus, a novel evaluation scheme is established. Firstly, in the Chang 8 Formation of Zhenjing area, Ordos Basin, China, horizontal rock samples (parallel to bedding) and vertical rock samples (perpendicular to bedding) are collected for designed rock electricity experiments and analyzed to obtain the corresponding horizontal and vertical Archie's parameters. Secondly, based on the detection characteristics of acoustic logging instrument and the transformation form of the Archie's equation, an estimation method of vertical resistivity is established. Combined with the horizontal resistivity obtained from conventional deep resistivity logs in vertical boreholes, an estimation method of resistivity anisotropy coefficient is proposed. Thirdly, the relationships between resistivity anisotropy coefficients and water saturations and porosities are analyzed using experiment results, and four adjacent vertical wells in the study area are processed using the estimated method. The processed results demonstrate the feasible of the estimation method. In addition, the cross well profile of resistivity anisotropy coefficient in the target formation is characterized, which shows the target formation is slightly resistivity anisotropic and slightly heterogeneous. The relationships between the resistivity anisotropy coefficient and commonly used logs are discussed, which contribute to rapid analyze the resistivity anisotropy of the target formation. Based on the above methods and processes, the novel evaluation scheme of resistivity anisotropy is established, which contributes to quantify and qualitatively analyze the resistivity anisotropy characteristics of formations in and between vertical boreholes. The application results show that it provides an alternative scheme for continuously evaluating the resistivity anisotropy in boreholes and cross well profiles by using conventional logs and experiments.

We analyze the sensitivity of a three-dimensional crosswell electromagnetic (EM) system based on finite-difference time-domain modeling. To reinforce the simulation accuracy of low-frequency electromagnetic propagation in a small space, we limit the boundary reflection energy by suppressing the numerical dispersion of convolutional perfectly matched layers. The numerical simulation results for an isotropic medium indicate that an anomalous conductivity will induce a large magnetic field strength disturbance at the receiving coils in the X- and Z-directions, and a relatively large absolute sensitivity is observed at the receiving coils in the Y-direction. The three-component signals are most sensitive to high-resistance anomalies. Through an experiment that involved moving an anomalous conductivity disturbance, comparison plots of dynamic residual hydrocarbon monitoring between wells and depth determinations of anomalous conductivities are provided. The optimal acquisition depth interval of the receiving coil in the homogeneous medium is given, and the effective exploration area and optimal modeling region of the crosswell EM system are obtained.

Irregular measurements may occur during the drilling process due to unconsolidated formation resulting in poor signal recordings by the logging tool. This affects the quality of data acquisition and the accuracy of elastic logs, such as density and velocity profiles, in reservoir characterization. It is of paramount importance to ensure the stability of the wireline-logging tool and to prevent compromising measurements of the formation's physical properties. While previous literature focused on the application of different machine learning (ML) algorithms for well logging, their application in a particular domain implied a narrow methodological utility for researchers. Therefore, this study combined two superior techniques of ML, supervised and unsupervised, for enhancing the elastic log response to ultimately help us to enhance reservoir characterization and interpretation. First, the density-based spatial clustering of applications with noise (DBSCAN) was used for outlier detection, and then, feature selection was used to identify highly correlated logs, which helped in rebuilding the density log. After successful ranking, the scaled-down features were carried forward to construct a regression model for density logs rebuilding. The comparative results confirmed the high accuracy of porosity estimated from rebuilt density log compared to that of core data. Consequently, it reduces cumbersome human efforts and time.

Many fractured reservoirs are dual porosity reservoirs including matrix pores and fractures, which both contribute to the porosity. However, previous studies either ignored the effect of pores or fractures on dual laterolog, or had special application conditions. Considering the joint contribution of pores and fractures, a response equation of dual laterolog in dual porosity reservoirs is established. First, based on a borehole model with a vertical fracture in dual porosity reservoirs, fracture porosity equations are established in the investigation depths of deep and shallow laterologs, respectively. Using Archie's equations, the response equations of deep and shallow laterolog resistivities are derived, respectively. Then, the response equation, which is similar to the traditional equation of dual laterolog in fractured tight reservoirs, is established. It contains three unknown parameters, i.e. fracture width, fracture cementation exponent and matrix cementation exponent. To solve the equation, a virtual core model is proposed to estimate the fracture cementation exponent by fracture width. The estimation method of matrix cementation exponents is established by empirical formulas. Then, the response equation can be transformed into a nonlinear equation with one unknown parameter of fracture width, and conventional iterative methods can be employed to solve it and obtain the fracture width. Using the response equation and the solution scheme, the change characteristics of fracture cementation exponents in different fractured reservoirs are first analyzed. It shows that the fracture cementation exponents increase with the fracture widths, and they decrease with the increase of the ratio of formation water resistivity to unfractured reservoir resistivity. The applications indicate that the equation and solution scheme can obtain relatively accurate fracture widths quickly in dual porosity reservoirs, and they are recommended for dual porosity reservoirs with high fracture angel (>70 degrees) and low fracture density (<1/3.78 m-1). The findings of this study can help better understanding of the different contributions of fractures and pores to dual laterolog responses in dual porosity reservoirs, and they can provide an alternative solution for evaluating dual porosity reservoirs.

The statistics for a window of an engineering observation is called running window quantity. Running intra-covariance is a special case of running inter-covariance, when two variables are the same. The inter-covariance minus the geometric average of respected intra-covariances is called the pure inter-covariance. It gives you a cleaner association analysis compared with the running Pearson analysis. The normalization of covariance to standard deviation is called the Pearson correlation coefficient. The covariance for the region above or below the average is called semi-covariance (upper or down). Here we present a pure inter running semi-covariance, an accurate ReLU (Rectified Linear Unit) way of measuring the inter-non-linear correlation between variables excluding the intra-non-linear components. Our framework is applied to successfully analyze the association between war factors and the gold response. The result of our analyses of the 12 years after the 2007 Mortgage crisis on the war equipment companies' stock versus the gold suggests that stocks from different regions have a slightly different impact on the gold value that reflects the overall peaceful economic prosperities.

The Wenchuan earthquake Fault Scientific Drilling venture (WFSD) offers an interesting possibility for examining the faulting and thermal system of the Yingxiu-Beichuan fault, Longmenshan fault zone, east edge of the Tibetan Plateau. However, the thermal and structural behaviour of the Yingxiu-Beichuan fault at the deeper level within the Sichuan Basin is still unknown. Therefore, the current study aims to delineate the thermal structure of the Yingxiu-Beichuan fault by employing the geothermal parameters along with the statistical concentration number (C-N) model, which has been previously neglected. Two accessible wells, WFSD-2 and WFSD-3, with their common gamma-ray (GR) records, were utilized to portray and measure the heat production (HP) and thermal conductivity (TC) extent in the Wenchuan earthquake zone. Besides, fifteen temperature logging curves run along different time period in WSFD-2 was utilized to measure to equilibrium temperature (ET). The ET was used to estimate the thermal gradient (TG). The TC and TG were multiplied to get the heat flow (HF). Consequently, the examination of 17,501 estimated data points in WFSD-2 through geothermal parameters shows that the Yingxiu-Beichuan fault (YBF) corresponds to low TC, low HF, and high HP values. The HP differs between 0.1 and 5.5 mu w/m(3), TC changes between 0.23 and 4.32 w/mk, and HF ranges amid 45-55 mW/m(2). In addition, a multi-fractal approach with the utilization of concentration number (C-N) fractal model technique and log-log plots were utilized for describing and separating the six population zones with different intensities using GR, HP, and TC. These six population zones represent the six formations of Xujiahe sedimentary formation and Pengguan complex igneous formation encountered in the WFSD-2. The comparison of geothermal parameters (GR, HP, and TC) with spectral gamma-ray (SGR) (U, Th, and K) also shows the presence of six population ranges. The results obtained by applying the C-N model and SGR are in agreement with each other, which shows the reliability of the research work. The corollary, the proposed methodology for the estimation of the thermal structure of faults, can be further utilized to numerous basins worldwide, which have the same geology of sedimentary and igneous rocks.

Azimuthal gamma-ray logging instruments play an important role in the geological steering technology (geosteering) of directional drilling engineering. While their logging response characteristics in various geology bodies are ambiguous, and a fast prediction methods of the distances between logging instruments and formation boudnaries, which are suitable for real time geosteering in drilling, are rare. In order to settle these problems, a Monte Carlo model is established based on the two-layer model and the multi-probe azimuthal gamma-ray logging (MPAGR) instrument structure. Monte Carlo N-Particle Transport (MCNP) Code is employed to simulate the logging responses of MPAGR when drilling into various geology structure bodies (layerd formations, fault, pinchouts and lens), which commonly drilled by honrizontal wells, and then a logging response characteristic scheme in eight spatial relationships between horizontal wells and formations is analyzed and summerized for guiding geosteering qualitatively. Based on these numerical simulations, a fast prediction method, a series of non-linear functions which can describe the relationships between logging responses and boundary distances, is proposed to guide realtime geosteering quantitatively. Finally, the qualitative scheme and the quantitative prediction method are applied in two horizontal wells in the Songliao Basin, China. The application results and effects demonstrate the availability of the scheme and the reliability of the fast prediction method. The scheme and the prediction method can provide reference and alternative solutions for real-time geosteering when drilling horizontal wells, especially in the formations where the geosteering function of resistivity logging is limited, e.g., shale gas/oil, coalbed methane, thin layer and low resistivity contrast reservoirs.

Metamorphic rocks are diverse with more compositions, structures, and textures that are complex. Rock type identification and prediction from metamorphic rocks using well log data are difficult tasks. This study shows the use of cross plot technique, Pearson correlation, and factor analysis in metamorphic rocks interpretation using borehole geochemical data from the 4390–5089 m interval depth of the Chinese Continental Scientific Drilling Main hole. Lithological identification abilities, correlation between geochemical and geophysical logs, and build a factor model which link in situ chemical element to minerals were studied. The results show that Potassium and Thorium logs are the most discriminating logs in metamorphic rocks. Pearson correlation shows that Potassium and Thorium are the largest contributors to the gamma ray responses. Factor analysis results show a 2 factor model-where factor 1 (amphibole mineral) and factor 2 (K-feldspar mineral) described 76.261% of the variation in log responses. These statistical methods can be a very helpful tool in helping the task of geoscientists in the context of research drillings.

This study proposes a novel approach to predict missing shear sonic log responses more precisely and accurately using similarity patterns of various wells with similar geophysical properties, which is important in decision making and planning of hydrocarbon exploration. Deep Neural Network (DNN) along with the similarity metrics such as Jaccard and Overlap similarities are employed to examine the relationship between the wells. Further, dimensionality reduction techniques including Multi-Dimensional Scaling (MDS) and well-ranking process are applied to extract common geophysical responses of the wells. A higher response indicates the existence of a strong similarity. This can also be verified by the superimposed of well log data. The potential benefits of our novel method are following; (a) it does not follow the zone-by-zone prediction of the missing logs such as rock physics methods, (b) it outputs the uncertainties facilitated that is by the least-squares method. Having the potential of demonstrating shear sonic log prediction in hydrocarbon-bearing zones, which cannot be precisely predicted by the Greenberg-Castagna method that only works in brine-saturated rocks, this approach will provide improved accuracy, where shear sonic logs are missing and need to be predicted for geomechanics, rock physics, and other applications.

Fractured tight sandstone reservoirs are dual-pore systems including matrix pores and natural fractures, which both contribute to the system permeability. However, most previous studies either calculated the matrix permeability or obtained the fracture permeability to represent the system permeability in the logging evaluation of fractured tight sandstones because existing logging methods cannot distinguish the two types. In this study, a novel method is proposed to estimate the system permeability in fractured tight sandstones using geophysical logs. First, the fracture characteristics in the Upper Triassic Chang 8 member of the Yanchang Formation, southwest Ordos Basin, China, were analyzed. Based on the hydraulic flow unit approach, the formation classification criteria and the corresponding permeability–porosity models were established; then, the pure matrix permeability in the dual-pore system was calculated using geophysical logs. Based on the fracture characteristics, the relative pure fracture permeability was obtained using the Sibbit and Faivre method. By applying the Parsons’ model in boreholes, the system permeability was then calculated by coupling the relationship between the two permeabilities. Finally, two field applications in the study area demonstrate the feasibility of the proposed method, and the logging responses, application effects and applicable conditions of this method are discussed in detail. These applications indicate that the proposed method is suitable for tight reservoirs with fracture widths less than 200 μm, and considered to be dual-pore systems.

The geological factors of any reservoir, such as porosity, lithology, and fluid characteristics, control the rock physics models. Several retrospective studies have discussed the methods of discriminating and calculating these geological factors in the Lower Goru sand reservoir. Therefore, it is imperative to build the best-modified suitable rock physics model and template to diagnose an inadequately consolidated reservoir that has been previously overlooked. For the purpose, the research evaluates a dataset of five wells to develop a modified rock physics model and template for a Lower Goru sand reservoir in Pakistan. The study compares and evaluates different rock physic models such as Stiff sand, Friable-sand, Greenberg and Castagna, and Raymer's model to calibrate the best model, which would comprehensively assist the future researchers. According to the results, Stiff sand is quite a suitable rock physics model. Furthermore, the rock physics template (RPT) is utilized to test the reliability of the predicted model that is helpful for the formation evaluation, reservoir characterization, and prospect evaluation across different fields of the Lower Goru sand reservoir. The predicted model can be further used to estimate porosity from the seismically derived impedance in the entire Lower Goru sand reservoir, Pakistan, and worldwide which has the same geological trends and reservoir distribution.

Crosswell electromagnetic (EM) method has fundamentally improved the horizontal detection ability of well logging and will become an increasingly promising approach for the secondary exploration of hydrocarbon reservoir. We applied orthogonal least squares (OLS) radial basis function neural network (RBFNN) based on improved Gram–Schmidt (G–S) procedure to three-dimensional (3D) crosswell EM inversion problems. In the inversion process of the simplified crosswell model with single-grid conductivity anomalies and normal oil reservoir, compared the inversion results of other five neural networks, OLS-RBFNN was proved to have the best global optimization ability and the fastest sample learning speed and the average inversion error of low conductivity anomalies model (4%) and oil reservoir model (9%) can meet the inversion requirements of crosswell EM method. Only the OLS-RBFNN could achieve ideal inversion results in the most concerned central area of crosswell model, and the inversion accuracy of this algorithm will be more outstanding when the model becomes more complex. Merely using the three-component time-domain crosswell EM data of two wells, the inversion of 3D medium conductivity in the crosswell dominant exploration area can be effectively realized through the nonlinear approximation of the OLS-RBFNN.

The application of the Cole-Cole model within time-domain induced polarization (TDIP) forward field modeling shows that the model parameters can characterize time-varying states of the TDIP field and support observed data analysis. The Cole-Cole model contains real and imaginary parts, and it requires a frequency-to-time conversion for TDIP forward modeling. However, the TDIP field is usually expressed by a real number, and its intuitive time-varying states field intensity increases with charging time. Therefore, the forward model should be constructed in a simpler form. We have aimed to develop a forward model using mathematical functions not based on physical principles. The Weibull (WB) growth model, which is primarily used to describe the time-varying curve features in regression analysis, is introduced into the basic algorithm of the TDIP forward model. Subsequently, a forward expression of the TDIP effect is established. Based on the time-varying shape and scale parameters, this expression describes the time-varying rate and relaxation states of the TDIP fields. Furthermore, based on the extensively used conjugate gradient optimization, an apparent WB parameter scheme is initiated to calculate the spectral parameters that represent the relaxation and time-varying rate obtained from the multi-time-channel TDIP data. Finally, this scheme is applied to interpret the different simulated and actual TDIP data. The results demonstrate that the WB growth model can be used for the TDIP forward model without involving physical principles, the model parameters without specific physical significance can be used to represent the time-varying states of TDIP fields, and apparent WB parameters can be used to discern different TDIP observed data. The setting of the TDIP forward model and model parameters can actually be more flexible and diverse, so as to obtain simpler forward expressions and ensure a highly efficient inverse solution.

In this paper, we propose a method for obtaining two-dimensional T1–T2 spectrum from simultaneous inversion of the MRIL-Prime tool, dual-TW logging data, in order to improve the accuracy in identifying gas-bearing reservoirs. This paper was accomplished by analyzing the theoretical feasibility of the method, verifying its effectiveness by numerical simulation, and then applying the method to actual logging interpretations to identify gas-bearing reservoirs. The practical application results show that this method can circumvent misidentification of reservoirs due to the presence of large pores—a known issue with using a one-dimensional differential spectrum—and effectively identify gas-bearing reservoirs with low resistivity.

This chapter presents the state of the art of the Intelligent Portfolio Theory which consists of three parts: the basic theory — principles and framework of intelligent portfolio management, the strength investing methodology as the driving engine, and the dynamic investability map in the confluence of business and market cycles and sector and location rotations. The theory is based on the tenet of “invest in trading” beyond “invest in assets”, distinguishing asset portfolio versus trading strategies and integrating them into a multi-asset portfolio which consists of many multi-strategy portfolios, one for each asset. The multi-asset portfolio is managed with an active portfolio management framework, where the asset allocation weights are dynamically estimated from a multi-factor model. The weighted investment on each single asset is then managed via a portfolio of trading strategies. Each trading strategy is itself a dynamically adapting trading agent with its own optimization mechanism. Strength investing as a methodology for asset selection with market timing focuses on dynamically tracing a small open cluster of assets which exhibit stronger trends and simultaneously follow trends of those assets, so to alleviate the drawbacks of single-asset trend following such as drawdown and stop loss. In the real world of global financial markets, the investability both in terms of asset selection and trade timing emerges in the confluence of business cycles and market cycles as well as the sector rotation for stock markets and location rotation for real estate markets.

On the basis of machine leaning, suitable algorithms can make advanced time series analysis. This paper proposes a complex k-nearest neighbor (KNN) model for predicting financial time series. This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition (EMD) for financial time series signal analysis and principal component analysis (PCA) for the dimension reduction. The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading. Finally, prediction is generated via regression on the selected nearest neighbors. The structure of the model as a whole is original. The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index, an individual stock, and the EUR/USD exchange rate.

Mudstone is very similar to shale except it lacks sheet bedding. Shale gas is widely concerned and successfully exploited commercially in the world, while gas-bearing mudstone is rarely paid attention. To evaluate the reservoir characteristics and exploitation potential of gas-bearing mudstone, a total of 127 mudstone samples from the Shanxi formation were tested by X-ray diffraction (XRD), scanning electron microscope (SEM), gas content, etc., and the qualitative identification and quantitative evaluation of gas-bearing mudstone reservoirs were performed on four wells using the logging curve overlay method and reservoir parameter calculation equations. The results showed that: (1) the average total gas content of core measurement is 1.81 m 3 /t, and the total content of brittle minerals is 44.2%, which confirms that mudstones can also have good gas content and fracturing performance; (2) logging evaluation the average thickness of gas-bearing mudstone is 55.7 m, the average total gas content is 1.6 m 3 /t, and the average brittleness index is 38.1%, which indicates that the mudstone of Shanxi formation in the study area is generally gas-bearing and widely distributed. All the results reveal that gas-bearing mudstone with block bedding has the same exploitation potential as shale with sheet bedding,which deserves more attention.

In reservoir evaluation, it is essential to estimate the saturation levels of natural gas hydrates. The conventional algorithms of gas hydrate saturation based on geological experience or physical equations are simple multi-physical models that often provide varying results in the absence of core testing, and more complicated is to consider the complexity and instability of gas hydrates reservoirs in permafrost areas. Based on Bayesian statistical theory and three reasonable assumptions of the ideal saturation model, we combined a Bayesian discriminant function and a hyperplane equation to derive and verify two new saturation algorithms: the Saturation algorithm from the Bayesian discriminant function considering a linear correlation (SBDF) and the Saturation algorithm from the Bayesian discriminant function considering the conventional saturation (SBDF-CS). In experiments based on theoretical model data and field logging data from the sandstone and mudstone layers in the Muli permafrost area, the SBDF and SBDF-CS algorithms yielded maximum average saturation error less than 11.1% and 12.2%, only through logging data without core test calibration. The SBDF algorithms rapidly and objectively utilize representative sample data with multi-physical signals. The SBDF algorithm can be used for the initial saturation estimation of gas hydrate in permafrost areas, and the SBDF-CS algorithm is more competent for the accurate calculation of saturation in the comprehensive research.