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Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D Modeling" e-course on the Moodle platform at TTK University of Applied Sciences, Tallinn, Estonia. By predicting trends, identifying patterns, and optimizing resource allocation, machine learning can improve the efficiency of e-learning and provide students with tailored recommendations for acquiring relevant knowledge and skills. The results of the study show that machine learning algorithms can be used to process the available e-course log data, using the clickstream of e-course resources and for their automated processing. The results suggest potential applications in personalized course recommendations, prediction and dropout prevention strategies, resulting in a more effective and personalized educational experience. Future research will focus on improving models of available registration data, exploring and using advanced machine learning techniques to improve the accuracy and usefulness of predictions, and providing faster recommendations to help students navigate their studies more effectively. [ABSTRACT FROM AUTHOR]

The application of the basin simulation method in reconstructing the pressure evolution process is frequently constrained by the limited availability of measured data. In order to establish a valid relation between logging data and paleo-pressure, this paper proposes a novel approach to constrain the pressure reconstructed by basin simulation method. The Eaton index N corresponding to the paleo-pressure of each period is calculated using inversion method, based on measured paleo-pressure by fluid inclusion PVTx method and logging data, in accordance with the Eaton formula. Subsequently, this value is utilized to calculate the paleo-pressure of other wells. The paleo-pressure results are compared with those reconstructed by basin simulation method to validate the reliability of the findings. The findings demonstrate a strong concurrence between the paleo-pressure calculated using the mean value of index N for each period and the reconstructed paleo-pressure obtained through basin simulation methodology, with an average error margin of less than ± 5%. Therefore, utilizing log data to constrain the paleo-pressure reconstructed by basin simulation method is a viable approach due to its ease of accessibility, strong continuity, and broad applicability and potential for application. This is particularly advantageous in regions where acquiring paleo-pressure data poses challenges. The paleo-dynamic conditions exert a significant influence on the distribution of gas and water in tight sandstone gas reservoirs, providing valuable insights for identifying favorable areas. [ABSTRACT FROM AUTHOR]

Macrolithotypes control the non-homogeneity of coal reservoirs, and it is important to grasp their distribution patterns for the exploration and development of coalbed methane (CBM). Traditional evaluation of macrolithotypes often relies on drilling cores and downhole workings, which are costly and difficult for obtaining comprehensive physical properties of coal seams. In recent years, geophysical logging technology has been commonly applied for evaluating physical properties of coal reservoirs because of its high efficiency and economic features. Taking the Junlian block in Sichuan province as a case study, based on many CBM evaluation wells and data analysis, a logging prediction method for the macrolithotypes of a high-rank coal reservoir is established. The problem of "boundary effect" is solved by the Haar wavelet analysis. Through a Pearson correlation coefficient matrix combined with backpropagation neural network analysis, density, natural gamma ray, acoustic time, compensated neutron, and deep lateral resistivity are extracted as critical parameters for constructing the method of logging interpretation of macrolithotypes. Logging identification models of different macrolithotypes are established by principal components analysis. Based on the results of logging interpretation, the macrolithotypes in the study area are mainly semi-dull coal and semi-bright coal. The genesis of macrolithotypes is analyzed by trace elements and lithologic characteristics. The bright coal in the study area is formed in a sedimentary environment of low paleotemperature, high anaerobic, saline water, and low sea level. In contrast, dull coal is formed in a sedimentary environment of relatively high sea level, brackish water, relatively rich oxygen, and relatively high paleotemperature. [ABSTRACT FROM AUTHOR]

Traditional machine learning models have mainly been used to study geological logging data of a single sample point, ignoring the fact that logging data has a strong spatial correlation. In this study, we use convolutional neural network to extract single-point features, structural features, and multidimensional features from logging data and compare the identification effects of lithology identification models based on the three features. The identification model based on the multidimensional feature extraction achieves 77.94% correctness in the test set, which is the best result among the identification models based on CNN and the three machine learning models. Based on this feature extraction model, the feature fusion modules in U-net and feature pyramid are added respectively to build two feature fusion models to combine the features extracted from different convolutional layers and improve the effectiveness of the model. The model also introduces attention mechanism to improve the role of useful features in the model training process. The identification accuracy of the two feature fusion models, U-CNN and P-CNN, reached 79.67% and 80.02% on the test set, respectively, which verified the effectiveness of the feature fusion models for lithology identification in the study area. [ABSTRACT FROM AUTHOR]

An interpretability model for intelligent lithology identification is proposed, which utilizes Ensemble Learning Stacking, Permutation Importance (PI), and Local Interpretable Model-agnostic Explanations (LIME) techniques. The aim of this method is to provide more accurate geological information and scientific support for oil and gas resource exploration. Two logging datasets from the public domain were used as experiments, and support vector machine (SVM), random forest (RF), and naive Bayes (NB) were employed as base learners, while SVM was utilized as the meta learner for lithology classification via stacking algorithm. The accuracy of the model was verified using evaluation metrics such as Area Under Curve (AUC), precision, recall, and F1-score. The PI and LIME techniques were employed to explain the lithology identification model. The results indicate that the stacking algorithm produced the best indexes and highest prediction accuracy. With respect to overall interpretation, PHIND, GR, and RT were found to have the most significant influence on lithology identification in a natural gas protection area in the United States, while DEN, CAL, and PEF were observed to be the most influential variables for lithology identification in the Daqing Oilfield in China. From the perspective of a single sample, the LIME algorithm can provide a quantitative prediction probability and degree of influence of the characteristic variables. [ABSTRACT FROM AUTHOR]

Prediction of the failure parameters, cohesion (C) and friction angle φ , is essential for drilling operation, field development, and hydraulic fracturing design. Several case studies have been conducted to predict these parameters. In all cases, empirical correlations were recommended based on experimental data where the issue of measurements' uncertainties remained a common factor in all of them. Hence, in this study, the artificial neural network (ANN) is employed to predict these parameters from readily available logging data of two wells. A sensitivity analysis showed a high correlation between bulk density (ROHB), compressional time (DTC), and neutron porosity (NPHI) with the friction angle and cohesion analysis. Hence, ROHB, DTC, and NPHI were selected as inputs for the ANN model. Comparing the measured cored data of cohesion and friction angle with those predicted from the ANN model, the results indicated a model error less than 3% average absolute percentage error (AAPE) and a correlation coefficient greater than 0.95. Moreover, an empirical correlation was developed and validated with another set of data, with AAPE less than 3% and R greater than 0.95. Comparison with the literature shows the better accuracy of the ANN in predicting cohesion and friction angle. The model is several times faster than the existing methods in-field application with high reliability and accuracy. [ABSTRACT FROM AUTHOR]

Revealing subsurface structures is a fundamental task in geophysical and geological studies. Logging data are usually acquired through drilling projects, which constrain the subsurface structure, and together with the description of drill core samples, are used to distinguish geological units. Clustering is useful for interpreting logging data and making log unit classification and is usually performed by manual inspection of the data. However, the validity of clustering results with such subjective criteria may be questionable. This study proposed the application of a statistical clustering method, the hidden Markov model, to conduct unsupervised clustering of logging data. As logging data are aligned along the drilled hole, they and the geological structure hidden behind such sequential datasets can be regarded as observables and hidden states in the hidden Markov model. When log unit classification is manually conducted, depth dependency of logging data is usually focused. Therefore, we included depth information as observables to explicitly represent depth dependency of logging data. The model was applied to the following geological settings: the accretionary prism at the Nankai Trough, the onshore fault zone at the Kii Peninsula (southwest Japan), and the forearc basin at the Japan Trench. The optimum number of clusters were searched using a quantitative index. The clustering results using the hidden Markov model were consistent with previously reported classifications or lithological descriptions; however, our method allowed a more detailed division of logging data, which is useful to interpret geological structures, such as a fault or a fault zone. Therefore, the use of the hidden Markov model enabled us to clarify assumptions quantitatively and conduct clustering consistently for the entire depth range, even for different geological sites. The proposed method is expected to have wider applicability and extensibility for other types of data, including geochemical and structural geological data. [ABSTRACT FROM AUTHOR]

Traditional lithology identification left the problems of low accuracy, recognition efficiency and generalization ability. Facing the logging data with outliers, unbalance and high complexity, we propose a lithology identification method based on an improved neighborhood rough set and AdaBoost. On the basis of the classical neighborhood rough set, the selection of the neighborhood radius and the running time are optimized. The redundant information in logging data is then effectively eliminated. Thus more sensitive logging curves are selected without changing the physical meaning of logging attributes. Then the selected data are input into the AdaBoost model to construct a lithology identification model. About 54,000 samples from 5 boreholes are tested in the study area. The accuracy of classification on the test set is about 98.42%. Compared with BP neural network and random forest algorithm, the proposed method owns advantages in recognition accuracy and generalization ability. It can provide help for complex lithology recognition in the study area. [ABSTRACT FROM AUTHOR]

Rock mechanical parameters are essential for drilling operation optimization, reservoir health monitoring, and hydrocarbon production. Consequently, achieving high economic revenue from the reservoir is dependent upon an accurate prediction of these parameters. Cohesion and internal friction angle are two important parameters that represent the shear strength of the rock. Several works have been done to predict these two parameters empirically from lab measurement or wireline logging data. In this study, two machine learning (ML) techniques, decision tree (DT) and random forest (RF), were used to build a predictive model using actual well logging data of two wells. The input data include compressional time (DTC), bulk density (ROHB), and neutron porosity (NPHI), as the sensitivity analysis showed that friction angle and cohesion are more sensitive to this input than the other logging parameters. The first well data set was used to train and test the model, while another set of 635 data points from different well was used for the model validation. The model accuracy was measured using the correlation coefficient (R), and the absolute average percentage error (AAPE), in addition to the failure parameter profiles. The result indicated the high accuracy of the two models in the prediction as the DT model recorded an R-value greater than 0.98 and 0.96 and an AAPE less than 2% and 3% for friction angle and cohesion, respectively, while the RF model showed its outperformance with R values 0.99 and 0.97 and AAPE values less than 1% and 2.5% for the two parameters respectively. The validation set confirmed the accuracy of the two models in generating a complete accurate profile of the friction angle and cohesion, as the DT model's R values were calculated at 0.96 and 0.95 and AAPE of 1.6% and 3.3% for friction angle and cohesion, respectively. On the other hand, the RF model showed its better accuracy with R values of 0.97 and 0.96, while the AAPE values were 1.2% and 2.5% for friction angle and cohesion respectively. The obtained results confirmed the high accuracy and the robustness of these techniques in predicting the friction angle and cohesion; in addition, these methods are several times faster for field applications. [ABSTRACT FROM AUTHOR]

Hydraulic fracturing of tight sandstone reservoirs is an important method of developing this type of unconventional oil and gas resources. The evaluation of in-situ stress distribution and rock mechanical parameters of fractured wells is the prerequisite for fracturing design. Therefore, the development of a calculation method to evaluate the stress distribution and mechanical parameters of rocks, based on a single well in-situ stress data, is the key to establish the optimal fracturing design. In this paper, based on the correlation between the laboratory experimental data and the logging response, we have plotted the initial stress profile and then applied the proposed method of combining density curve and natural gamma curve to correct the calculation results. In the case of a limited number of wells, the obtained profile correction model demonstrated satisfactory matching with laboratory experimental data. [ABSTRACT FROM AUTHOR]

Water-bearing structures in front of coal mine shaft drivages require effective advanced prediction. Based on the characteristics of coal field logging curve carrying rich geological information, it is introduced into the advanced prediction of water-bearing bodies in mine shafts. At the same time, mine geophysical prospecting technology, such as the DC resistivity method, can accurately and effectively track and detect geological conditions during mine shaft drivage. The proposed advanced prediction method of the borehole-ground electrical method based on the detailed interpretation of logging curves was experimentally studied using physical simulations of the detailed interpretation of water-bearing rock strata in logging data and geophysical advanced detection simulations of water-bearing structures in mine shafts. By constructing a physical model of the mine shaft drivage, the change in the water content of the rock strata is simulated by adding water to sand and gravel, and the apparent resistivity logging in a borehole is simulated. The water-bearing characteristics of the model under different water content conditions are interpreted by using 3-D visualization technology, and it is concluded that the change law of the electrical characteristics of sand and gravel after adding water is a logarithmic function relationship. Then, based on the results of the detailed interpretation of the logging curves, a model experiment using the DC resistivity method of ground-borehole joint advanced detection is carried out, and the response characteristics of changes in the model water diffusion resistivity are accurately detected. In addition, a shallow geological borehole is constructed in the field. Water injection in the borehole causes anomalous changes in the electrical properties of the strata around the borehole and simulates water-bearing structures at different depths and layers, which verifies the effectiveness of the borehole-ground electrical method in detecting water-bearing structures in front of mine shaft drivages. The simulation results show that the advanced prediction method of borehole-ground electromagnetic technology based on the interpretation of logging curves enhances the effectiveness and detection efficiency of advanced detection and prediction of the water-bearing characteristics of rock strata. Based on the logging curve, the water-bearing characteristics of the strata in front of a coal mine shaft drivage in the Dongsheng coalfield in Inner Mongolia are interpreted in detail, and the provided anomalous target areas can provide reliable guidance for advanced detection using the borehole-ground electrical method in the future. [ABSTRACT FROM AUTHOR]

High-quality hydrocarbon source rocks are present in the upper Cretaceous layer in the western slope of the Songliao Basin. Oil and gas have accumulated in these rocks at the shallow edge of the basin, which has led to the formation of oil sand resources. This study uses the back-propagation (BP) neural network method to predict the distribution of oil sand reservoirs and is the first study of its kind in China. First, based on the basic data collected by core sample, well log and geochemical analyses, and the reasonable selection of samples, the cores are divided into mudstone, siltstone, fine sandstone, medium sandstone, and sand, according to lithology. Second, a three-layer BP neural network model is constructed with two hidden S-type layers and one linear output layer. Third, through a comparison of the effect of different numbers of training sessions of the sample data, we demonstrate that the accuracy of the model can be increased to 90% after training the network 100,000 times. Then, the log-derived data of rocks with unknown lithologies are input into the neural network to predict whether they contain oil sands. We show that the BP neural network method can predict the distribution of oil sand reservoirs in the target horizon of the study area, and the results are consistent with research results on the corresponding sand reservoirs and sedimentary facies. Thus, we conclude that it is feasible to use the BP neural network method to predict the distribution of oil sand reservoirs. [ABSTRACT FROM AUTHOR]

Sand production is a major problem that the oil and gas industry has been facing for years. It can lead to loss of production, equipment damage or complete well abandonment. Prediction of sand has been historically challenging due to the periodic nature of sand production, insufficient laboratory tests and lack of field tests validation. Analyses have been performed to identify weak zones for planned wells, and common technique is the application of shear modulus and mechanical properties log (MPL) criteria developed by Tixier et al. (J Pet Technol 27:283–293, 1975). However, the set criteria have been found to be generally inadequate to detect transition zone or predict weak formation in some fields. In this study, using the knowledge of rock behavior, geomechanical properties and well log data, we have established new simple criteria for identifying fragile sections within a transition zone. In situ logging data from a field X, located in Sabah, Malaysia, and Field Y, located in Shimokita, Japan, were used in this study. Using the threshold for shear modulus and MPL, the criteria for the geomechanical properties are set to differentiate formation strengths at different depths. The threshold for Poisson's ratio is 0.34, Young's modulus at 1.6 × 106 psi and the unconfined compressive strength at 2400 psi. The MPL and geomechanical models were generated to predict sanding incident. The results were subsequently validated with artificial neural network using MATLAB. Also, critical wellbore pressure is calculated and acts as a guide to operate outside the sand failure envelope. Thus, the prediction of the weak formation using geomechanical properties has been further established in this study. [ABSTRACT FROM AUTHOR]

Predicting the fracture porosity of tight fracture reservoirs represents a difficult problem for log interpretation, although studying conventional log responses of tight fracture reservoirs provides helpful insights. Comparisons and analyses of the log response characteristics of the natural fracture zone of a tight reservoir are performed, and the results indicate that the acoustic (AC) and density (DEN) data are highly sensitive to fractures. First, the log response of the AC and DEN data are sensitive to tight fracture reservoirs, and differences in the AC and DEN log responses are obvious. Second, based on these differences, a model for calculating the fracture porosity is constructed, and an improved Willie difference method for the AC and DEN responses is proposed to calculate the fracture porosity. Finally, we apply the methodology to a case study of a tight reservoir in the Sichuan Basin, China, and then calculate the AC-DEN correlation coefficient and fracture porosity. The calculated fracture porosity is consistent with the fracture porosity from the full borehole micro-resistivity imaging results and core porosity. The results indicate that the method is applicable for predicting the porosity of fractures. [ABSTRACT FROM AUTHOR]

One of the most important tasks of evaluating natural resources of petroliferous basins is to determine the organic matter abundance of source rocks in the basin. The usual method for assessing the organic carbon content of source rocks is based on laboratory analyses. There is a deviation in calculating organic carbon content due to the heterogeneous distribution of organic matter and the artificial factors when sampling. According to the continuous characteristics of information logging, the conventional logging curves (mainly acoustics and resistivity, etc.) were calibrated with the organic carbon experimental data of cores, cuttings or sidewall cores. The organic carbon content of source rocks of the 4 (Es) and 3 (Es) members of the Shahejie Formation in western sag in the Liaohe depression was estimated directly by a great amount of continuous data including resistivity and acoustic logging, etc. Comparison between the results from computer processing and lab analysis of logging data shows that the organic carbon contents derived from the computer processing of logging data have the same reliability and accuracy as the lab analysis results. The present data show that this method is suitable to evaluate the source rocks of western sag in the Liaohe depression and has great potential in evaluating natural resources of sedimentary basins in the future. On the basis of logging data of source rocks, experimental data and existing geochemical analyses of the Liaohe Oilfield, the corresponding total organic carbon (TOC) isograms of source rocks were plotted. The source rocks of Es and Es of the Shahejie Formation are thought to be beneficial to hydrocarbon accumulation due to the high TOC. [ABSTRACT FROM AUTHOR]

Examining several sources of data on smartphone use, this paper presents evidence for the popular conjecture that mobile devices are 'habit-forming.' The form of habits we identified is called a checking habit: brief, repetitive inspection of dynamic content quickly accessible on the device. We describe findings on kinds and frequencies of checking behaviors in three studies. We found that checking habits occasionally spur users to do other things with the device and may increase usage overall. Data from a controlled field experiment show that checking behaviors emerge and are reinforced by informational 'rewards' that are very quickly accessible. Qualitative data suggest that although repetitive habitual use is frequent, it is experienced more as an annoyance than an addiction. We conclude that supporting habit-formation is an opportunity for making smartphones more 'personal' and 'pervasive.' [ABSTRACT FROM AUTHOR]

Valuable industrial oil and gas were discovered in the formations of Ordovician, Carboniferous and Triassic of the Tahe (塔河) oilfield, Xinjiang (新疆), China. The Carboniferous formations contain several oil- and gas-bearing layers. The lateral distribution of Carboniferous reservoir is unstable, and thin layers are crossbedded. This makes it difficult to do lateral formations’ contrast and reservoir prediction, so it is necessary to develop a method that can achieve reservoir lateral contrast and prediction by using multi-well logging data and seismic data. To achieve reservoir lateral contrast and prediction at the Carboniferous formations of the Tahe oilfield, processing and interpretation of logging data from a single well were done first. The processing and interpretation include log pretreatment, environmental correction and computation of reservoir’s parameters (porosity, clay content, water saturation, etc.). Based on the previous work, the data file of logging information of multi-well was formed, and then the lateral distribution pictures (2D and 3D pictures of log curves and reservoir parameters) can be drawn. Comparing multi-well’s logging information, seismic profiles and geological information (sedimentary sign), the reservoir of the Carboniferous in the Tahe oilfield can be contrasted and predicted laterally. The sand formation of Carboniferous can be subdivided. The results of reservoir contrast and prediction of the Carboniferous formations show that 2D and 3D pictures of multi-well reservoir parameters make the lateral distribution of reservoir and oil-bearing sand very clear, the connectedness of the reservoir of neighboring wells can be analyzed, and five sand bodies can be identified based on the reservoir’s lateral distribution, geological information and seismic data. [ABSTRACT FROM AUTHOR]

It has always been the focus of researchers to accurately identify gas hydrate location. Geophysical prospecting is a widely used method for gas hydrate exploration, which has high credibility, especially seismic exploration technology is most generally used. In our study, we analyze the different physical properties of gas hydrate and other minerals bearing in unconsolidated marine sediments based on the effective medium theory. Then, a new attribute is put forward to discriminate gas hydrate. The logging data in the Dongsha area of the South China Sea and the Hydrate Ridge along the Oregon continental margin are applied to validate this method. Our test results are basically in line with actual situation, which provides a new approach in hydrate identification. [ABSTRACT FROM AUTHOR]

The shallow accretionary prism of the Nankai Trough is a location where both large interplate earthquakes and slow earthquakes occur. Since the physical properties of sedimentary materials are important topics for understanding the structure of the prism, numerous ocean drilling expeditions have been conducted in that region to obtain logging data and core samples. Although the physical properties of the obtained samples are normally measured onboard immediately after coring, estimations of in situ physical properties are difficult because of differences in laboratory and in situ physical conditions. Herein, we propose a new method for estimating in situ porosity from downhole electrical resistivity log data that evaluates in situ porosity and thermal structure simultaneously using correlations between the porosity and resistivity, and between the porosity and thermal conductivity that were established based on laboratory measurements. When constructing physical property correlations, X-ray computed tomography data play an important role in estimating the porosity of samples from which resistivity or thermal conductivity were measured. To validate our method, we compared the estimation with density log data collected at Site C0002 and found that the estimated in situ porosity shows good agreement with the in situ porosity converted from density log data. A comparison with porosity measured onboard for core and cutting samples showed that they are consistent with each other. With this new method, continuous distributions of in situ porosity and thermal structure can be estimated simultaneously based on resistivity log data and heat flow, which are basic quantities acquired during ocean drilling science expeditions. [ABSTRACT FROM AUTHOR]