Summary: Geophysical logging is widely used in lithofacies identification, reservoir parameter prediction, and geological modeling. However, it is common to have well-log sections with low-quality and/or missing segments. Repeating the well-log measurements is not only expensive but might also be impossible depending on the condition of the borehole walls. In these situations, reliable and accurate well-log prediction is, therefore, necessary in different stages of the geomodeling workflow. In this study, we propose a time series regression model to predict missing well-log data, incorporating facies information as an additional geological input and using discrete wavelet transform (DWT) to denoise the input data set. The main contributions of this work are threefold: (i) We jointly use facies information with well logs as the input data set; (ii) we use DWT to denoise the input data and consequently improve the signal-to-noise ratio of the input data; and (iii) we regard the depth domain as the time domain and use a time series regression algorithm for log reconstruction modeling. We show a real application example in two distinct scenarios. In the first, we predict missing well-log intervals. In the second, we predict complete well logs. The experimental results show the ability of the proposed prediction model to recover missing well-log data with high accuracy levels.

Electrical resistivity (ER), induced polarization (IP), and self-potential (SP) are three geophysical methods that have been broadly used in the realm of mineral exploration. These geophysical methods provide complementary information, each exhibiting a distinct sensitivity to various types of mineral deposits. Considering the relationship among these three methods, we propose an integrated approach that merges their respective information to offer an improved localization technique for ore bodies. First, we invert the electrical conductivity distribution through electrical resistance tomography (ERT). Then, we use the inverted conductivity distribution to invert the IP and SP data in terms of chargeability and source current density distributions. Then, we normalize the inverted chargeability and source current density distributions and we combine them to obtain an ore body index (ORI) $\chi$ used to delineate the potential locations of ore deposits. We design this index to be sensitive to the presence of ore bodies, which are reflected by either strong and localized source current density (SP) and/or strong chargeability values (IP). The proposed method is first validated using a synthetic model with two distinct anomalies characterized by different properties. The results show the limitation of individual inversion, as each method exclusively detects one of these anomalies. The combined approach allows a better characterization of the target. Then, the approach is applied to a sandbox experiment in which two metallic bodies are buried in water-saturated sand used as the background. Again, the proposed methodology is successfully applied to the detection of the metallic targets, improving their localization compared with individual methods.

In this study, we aimed to detect the leakage inlets and determine the leakage pathways through a dam, and ensure that the amounts of leakage upstream and downstream are consistent and that there is no leakage around the concrete‐face rockfill dam. The injection of a pseudo‐random current in the water between two aluminum sheets A and B generates a current field that can be measured in water with sensitive current sensors. When the current is channeled along the leakage pathways, the flow field fitting (FFF) method can be used to detect their inlets. We first review the background equations for the seepage and current fields in the FFF method. Then we use the index‐velocity method to calculate the discharge and ensure consistency between the upstream and downstream leakage based on the Doppler effect. Moreover, to illustrate how the FFF method works, we used a numerical simulation to verify the feasibility of the FFF method. Finally, we proceed with a case study in which the FFF method and measured acoustic Doppler velocity are used to identify and map potential leakage pathways bypassing upstream into the downstream flow‐measurement weir. The proposed methodology successfully detected three leakage areas and obtained the discharge in a case study; our results provide engineering geology information for optimizing the layout of grouting holes.

Summary: 3D simulation of sedimentary facies using seismic data is vital for reservoir evaluation and estimation of oil and gas reserves. As a result of the high nonstationarity of sedimentary facies and the highly nonlinear characteristics of seismic attributes, the mapping relationship with sedimentary facies has certain ambiguities and uncertainties that affect the modeling results of sedimentary facies. Multipoint geostatistics (MPS) has proven to be an effective technique for modeling subsurface geological bodies. However, the conventional MPS only deals with stationary applications, and its capability of revealing the distribution of sedimentary facies is thereby limited. In addition, the task of seismic attributes selection, which significantly affects the performance of the modeling method in simulating the distribution of sedimentary facies, is difficult because the relationship between sedimentary facies and seismic attributes is complex. This article presents a nonstationary modeling method for simulating the distribution of sedimentary facies, which is featured by the multiscale spatial feature of patterns. In particular, the spatial location of the patterns is introduced as auxiliary information in the classification and simulation processes. The method incorporates multiscale results during the modeling procedure. Patterns from the multicategory training images (TIs) are classified by the optimized workflow. The seismic attribute selection is achieved by using fuzzy-rough sets. The proposed simulation method is verified by two typical TIs, followed by applications to predict the actual distribution of sedimentary facies. Compared with the filter-based pattern simulation (FILTERSIM) approach, the proposed simulation method is applicable for revealing detailed subsurface models, especially under complex geological conditions and limited information.

Summary: Reservoir lithofacies type is an important indicator of reservoir quality and oiliness, and understanding lithofacies type can help geologists and engineers make informed decisions about exploration and development activities. The use of well-log data to establish machine learning models for lithofacies identification has gained popularity; however, the assumption that data are independent identical distribution followed by these models is often unrealistic. Additionally, there is a possible incompatibility between the training and test data in terms of feature space dimensions. We propose the heterogeneous domain adaptation framework for logging lithofacies identification (HDAFLI) to address these problems. The framework comprises three main contributions: (i) The denoising autoencoder feature mapping (DAFM) module is adopted to resolve the incompatibility issue in feature space between training and test data. The connection between training and test data can be effectively established to improve the performance and generalization ability. (ii) The transferability and discriminative joint probability distribution adaptive (TDJPDA) module addresses the issue of data distribution differences. It improves the transferability of training and test data by minimizing the maximum mean difference (MMD) of the joint probabilities of the source and target domains and enhances their discriminative ability by maximizing the joint probability MMD of different lithofacies categories. (iii) Bayesian optimization is used to optimize hyperparameters in the light gradient boosting machine (LightGBM) model for high computational efficiency in determining the best accuracy. We selected well-logging data from eight wells in the Pearl River Mouth Basin of the South China Sea to design four tasks and compared HDAFLI with various baseline machine learning algorithms and baseline domain adaptive algorithms. The results show that HDAFLI has the highest average accuracy among the four tasks. It is 19.76% and 8.94% higher than the best-performing baseline machine learning algorithm and baseline domain adaptive method among the comparison algorithms, respectively. For HDAFLI, we also conducted ablation experiments, time cost and convergence performance analysis, parameter sensitivity experiments, and feature visualization experiments. The results of ablation experiments show that the three modules of HDAFLI all play an active role, working together to achieve the best results. In addition, HDAFLI has a reasonable time cost, can become stable after several iterations, and has good convergence performance. The results of parameter sensitivity experiments confirm that the accuracy of HDAFLI does not change significantly with changes in hyperparameters, which is robust. The results of feature visualization experiments show that the data of the training set and the test set are concentrated together to a certain extent, which indicates that HDAFLI has completed the task of data distribution alignment very well. The findings of this study can help for a better understanding of how to address the challenge of reservoir lithofacies identification through a heterogeneous domain adaptation framework. By solving the problem of feature space incompatibility and data distribution difference between training data and test data, the application of HDAFLI provides geologists and engineers with more accurate lithofacies classification tools. This study has practical application value for reservoir quality assessment, oiliness prediction, and exploration and development decision-making.