Lithology Recognition Research Articles

Joint sound denoising with EEMD and improved wavelet threshold for real-time drilling lithology identification

The present study proposes a joint denoising method for real-time drilling lithology identification based on drilling sound signals. The proposed method employs ensemble empirical mode decomposition (EEMD) for preliminary sound signal denoising while retaining the effective intrinsic mode function (IMF) components, which then undergo secondary denoising using the improved wavelet threshold function. Finally, the sound signal is reconstructed using the denoised IMF components. The experimental results show that the proposed method achieves a higher signal-to-noise ratio and lower root mean square error, reaching 12.94 and 0.0442, respectively. Furthermore, the proposed method demonstrated good performance in denoising rock drilling sound signals and successfully addressed issues including the susceptibility of sound signals to external interference, nonadaptive threshold selection in wavelet threshold denoising, and high reconstruction errors during EEMD in drilling environments. Simulation results show that the proposed method significantly improves the accuracy of lithology recognition by 23%, with a resulting accuracy of 72.58%.

LithoSegNet: Regional attention-based deep fusion of multi-scale and cross-stage features for real-time lithology segmentation

Lithology identification plays an important role in engineering construction, disaster prediction, reservoir evaluation, and other fields. However, with the development of mechanization and automation, traditional lithology recognition methods are gradually unable to meet existing demands in terms of recognition speed and efficiency. In order to achieve real-time, fast, and accurate identification of lithology, we designed a lightweight model backbone, proposed a multi-scale feature extraction module and a cross-stage feature fusion module based on regional attention, developed a step-wise fusion scheme for multi-stage feature, and developed a real-time lithology segmentation model. The experimental results show that the feature extraction module improves the mIoU of the model by 0.0021; The cross-stage feature fusion module improves the mIoU of the model by 0.0104; The step-wise fusion scheme can effectively fuse shallow spatial features and deep semantic features. The model mIoU using this scheme is 0.0246 and 0.0145 higher than that using SegFormer and SegNeXt fusion schemes, respectively. Our model LithoSegNet can achieve a mIoU of 0.9583 with a speed of 116.25 images per second, comprehensively surpassing many state-of-the-art models. The research results can provide technical support for automated geological sketching in field exploration and on-site construction, and lay the foundation for intelligent construction of underground engineering, which has important scientific and engineering application value.

An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks

Lithology identification is a critical component of reservoir evaluation and hydrocarbon development. However, the continental shale rocks, characterized by complex lithologies and developed laminations, present new challenges to traditional lithology identification techniques. In this study, we develop a centimeter-level intelligent identification system for continental shale lithology, leveraging digital image preprocessing and deep learning methods. Specifically, we construct a dataset from 400 m of drilling cores, encompassing 24 types of lithology and 152 lithological transitions. The downsampling and oversampling methods are employed to balance the dataset. The digital image denoising and restoration techniques are then utilized to enhance rock image features. Different neural network models are examined to obtain the optimum predictive results. The results indicate that data balancing improves the TOP1 and TOP5 accuracies by 10% and 5%, respectively. Gaussian low-pass filter denoising, in comparison to other methods, increases the TOP1 and TOP5 accuracies by 2%, establishing it as the superior denoising technique for lithology recognition. Considering both computational efficiency and prediction accuracy, EfficientNet emerges as the most effective model for continental shale image recognition. The optimized model attains up to 60% TOP1 and 95% TOP5 across 22 categories in drilling core samples. This study delves into the intelligent identification of macroscopic drilling core images of continental shale, and the findings can offer technical support for the interpretation and evaluation of continental shale reservoirs.

A Lithology Recognition Network Based on Attention and Feature Brownian Distance Covariance

In the context of mountain tunnel mining through the drilling and blasting method, the recognition of lithology from palm face images is crucial for the comprehensive analysis of geological conditions and the prevention of geological risks. However, the complexity of the background in the acquired palm face images, coupled with an insufficient data sample size, poses challenges. While the incorporation of deep learning technology has enhanced lithology recognition accuracy, issues persist, including inadequate feature extraction and suboptimal recognition accuracy. To address these challenges, this paper proposes a lithology recognition network integrating attention mechanisms and a feature Brownian distance covariance approach. Drawing inspiration from the Brownian distance covariance concept, a feature Brownian distance covariance module is devised to enhance the network’s attention to rock sample features and improve classification accuracy. Furthermore, an enhanced lightweight Convolutional Block Attention Module is introduced, with upgrades to the multilayer perceptron in the channel attention module. These improvements emphasize attention to lithological features while mitigating interference from background information. The proposed method is evaluated on a palm face image dataset collected in the field. The proposed method was evaluated on a dataset comprising field-collected images of a tunnel rock face. The results illustrate a significant enhancement in the improved model’s ability to recognize rock images, as evidenced by improvements across all objective evaluation metrics. The achieved accuracy rate of 97.60% surpasses that of the current mainstream lithology recognition neural network.

Research on Intelligent Recognition Technology in Lithology Based on Multi-parameter Fusion of Logging While Drilling

In oil and gas drilling, timely and accurate identification of formation lithology is an important guarantee of drilling safety. Aiming at the problems of inaccurate identification of lithology in drilling by traditional methods, and low efficiency due to the fact that even modern instruments cannot respond to lithology in real time. the Categorical Boost (CatBoost) model was applied to lithology identification in this study. However, since CatBoost uses more hyperparameters in its modeling, it is difficult to optimize model prediction by manually tuning the parameters. Therefore, the introduction of Kernel Principal Component Analysis (KPCA) extracts fewer and more important features from the original data, eliminates the redundant information contained therein, and combines with Bayesian Optimization (BO) algorithm to optimize the hyperparameters during the training process, thus improving the prediction performance of CatBoost. Two experiments were designed to verify the recognition ability of the model, and the final test results of the model showed that the KPCA-BO-CatBoost model proposed in this paper had the best overall performance, and the lithology recognition accuracy reached more than 90%. The model was effective in identifying the formation lithology, realized real-time lithology identification by combining the parameters of logging while drilling, improved the efficiency and accuracy of lithology identification, and was of great significance in guiding the subsequent drilling work.

Lithological identification based on high-frequency vibration signal analysis

Lithology changes affect drilling efficiency and safety during drilling. At present, lithology is usually identified by analyzing logging data in engineering applications. There is a certain lag due to the limitation of logging instrument installation location. This paper proposes a new rock formation identification method, which bases on high-frequency measurement sensors to record the vibration of drilling tools, and extracts the time and frequency-domain features of data. Then neural network is used to establish the lithology recognition model, so as to identify the rock formation change by using vibration signal. The method has been verified by field experiment. A lithology identification model is established by using the features of vibration signal. And average recognition accuracy of the model is 89.57%. The model accurately identifies the Soil layer, Sandstone, Strongly weathered siltstone and Medium-weathered siltstone. The identification results are in good agreement with the geological information.

Multi-scale spatiotemporal feature lithology identification method based on split-frequency weighted reconstruction

The accurate identification of lithology in thin and deep layers is a crucial task in logging. However, traditional logging lithology identification methods are often inefficient for thin and deep layers and sometimes require human intervention, which greatly reduces the efficiency of oil and gas production. Therefore, this study proposes a multi-scale spatiotemporal feature lithology identification method under split-frequency weighted reconstruction. First, split-frequency weighted reconstruction of the logging curves is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to improve their thin layer resolution. Subsequently, a feature fusion model of multi-scale convolutional neural networks and bidirectional gated recurrent neural networks (MCNN-BIGRU) is constructed to learn the spatiotemporal features of the logging curves. Finally, during feature propagation, the attention mechanism assigns weights to historical features to mitigate the accumulation of error information, thereby improving the lithology recognition effect. To verify the model's performance, we constructed a lithology dataset with five wells and conducted an experiment to show that the reconstructed logging curves have significantly higher vertical resolution than the original curves. Furthermore, the MCNN-BIGRU-AT model developed in this study exhibited a better lithology identification effect than the single-structure model, with a highest accuracy of 96.69%. In summary, the proposed method is a novel and efficient method for lithology identification.

Reservoir Lithology Identification Based on Multicore Ensemble Learning and Multiclassification Algorithm Based on Noise Detection Function.

Reservoir lithology identification is an important part of well logging interpretation. The accuracy of identification affects the subsequent exploration and development work, such as reservoir division and reserve prediction. Correct reservoir lithology identification has important geological significance. In this paper, the wavelet threshold method will be used to preliminarily reduce the noise of the curve, and then the MKBoost-MC model will be used to identify the reservoir lithology. It is found that the prediction accuracy of MKBoost-MC is higher than that of the traditional SVM algorithm, and though the operation of MKBoost-MC takes a long time, the speed of MKBoost-MC reservoir lithology identification is much higher than that of manual processing. The accuracy of MKBoost-MC for reservoir lithology recognition can reach the application standard. For the unbalanced distribution of lithology types, the MKBoost-MC algorithm can be effectively suppressed. Finally, the MKBoost-MC reservoir lithology identification method has good applicability and practicality to the lithology identification problem.

Research on Lightweight Lithology Intelligent Recognition System Incorporating Attention Mechanism

How to achieve high-precision detection and real-time deployment of the lithology intelligent identification system has significant engineering implications in the geotechnical, geological, water conservation, and mining disciplines. In this study, a lightweight lithology intelligent identification model is proposed to overcome this problem. The MobileNetV2 model is utilized as the basic backbone network to decrease network operation parameters. Furthermore, channel attention and spatial attention methods are incorporated into the model to improve the network’s extraction of complicated and abstract petrographic elements. In addition, based on the findings of network training, computing power performance, test results, and Grad-CAM interpretability analysis and comparison tests with Resnet101, InceptionV3, and MobileNetV2 models. The training accuracy of the proposed model is 98.59 percent, the training duration is 76 min, and the trained model is just 6.38 megabytes in size. The precision (P), recall (R), and harmonic mean (FI-score) were, respectively, 89.62%, 91.38%, and 90.42%. Compared to the three competing models, the model presented in this work strikes a better balance between lithology recognition accuracy and speed, and it gives greater consideration to the rock feature area. Wider and more uniform, strong anti-interference capability, improved robustness and generalization performance of the model, which can be deployed in real-time on the client or edge devices and has some promotion value.

Lithology Logging Recognition Technology Based on GWO-SVM Algorithm

Accurate identification of lithology is the basis and key process of fine logging interpretation and evaluation. However, reservoirs formed by different sedimentary environments and tectonic movements generally have the characteristics of complex and diverse lithology and strong heterogeneity, which brings great difficulty to the identification of reservoir lithology. This paper proposes an automatic identification technology for lithology logging based on the GWO-SVM algorithm model. The technology is actually applied, and the results are compared with the results of the support vector machine cross-validation optimization model, PNN (probabilistic neural network) model, and ELM (extreme learning machine) model. The results show that the GWO-SVM lithology logging recognition model can efficiently solve the lithology recognition and classification problems in complex reservoir analysis and has strong adaptability and higher recognition accuracy.

Identification of Rock Properties of Rock Wall Cut by Roadheader Based on PSO-VMD-LSSVM

The problem of low digging efficiency and mining imbalance due to outdated digging technology and low degree of equipment intelligence has long existed in coal mine roadway excavation work. Lithology identification is the key to the intelligence of roadheading equipment. Accurate lithology identification significantly affects the automatic control of roadheader cutting conditions. Completing the identification of lithology in the process of rock wall cutting by a roadheader involved the following steps: building a tunneling experiment platform, making four rock specimens with different lithologies, completing the tunneling simulation experiments on four lithologies, obtaining current sensor data of four lithologies cutting, and finally proposing an intelligent lithology identification method of PSO-VMD-LSSVM. The research results show that the particle swarm algorithm (PSO) optimized the variational modal decomposition (VMD) with minimum envelope information entropy as the fitness function can realize the adaptive decomposition of the current signal of truncated motors. The signal reconstruction can increase the signal-to-noise ratio of the current signal by selecting the eigenmodal components according to the energy density and correlation coefficient criterion. The multi-scale fuzzy entropy is used as the eigenvector of the reconstructed current signal as the fuzzy entropy of different lithology cut-off motor currents has better differentiation at different scales. The least-squares support vector machine (LSSVM) is used to classify the feature vectors processed by custom decomposition parameter VMD and gives a recognition rate of 87.5%. The recognition rate increases to 97.5% for the feature vectors processed by PSO-VMD. The particle swarm algorithm optimizes the noise reduction via VMD to effectively improve the lithology recognition rate. The research results can provide a methodological reference for rock property recognition during rock cutting by a roadheading machine.

Adaptive Multiexpert Learning for Lithology Recognition

Summary Lithology recognition based on well logging plays an important role in drilling optimization and minimizing investment costs. Many existing lithology recognition methods resolve this problem by assuming that they have a balanced lithology data set (i.e., the different classes of lithology data samples have an even distribution). However, this assumption is usually not feasible in the real world, where the data sample size varies enormously between different categories of lithology and thereby leads to significantly degrading recognition performance. To address this problem, we propose adaptive multiexpert learning (AMEL) in this paper. Reweighting methods can be used to solve imbalanced distributions. However, blindly increasing the loss weights of the minority class does not create a balanced model. However, AMEL incorporates reweighting into knowledge distillation (KD) to further optimize the learning process. Our process involves dividing the training set into several groups and training various expert models. Then, we trained student models using a combination of weighted instance losses and distillation losses. Furthermore, we carried out extensive experiments on real-life log data to prove the efficiency of AMEL.

A framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes

Lithology identification is the basis of energy exploration and reservoir evaluation, intelligent and accurate identification of underground lithology is a key issue. The establishment of a machine learning lithology identification model using logging data is a hot research direction in recent years. However, the logging data has a high degree of non-linearity and multi-response characteristics, and there are insufficient numbers of labeled samples in the training data set. These will eventually affect the modeling accuracy and may cause over-fitting. Therefore, a framework of active learning and semi-supervised learning for lithology identification based on improved naive Bayes (ALSLINB) is proposed. The contributions are fourfold: (i) The Gaussian mixture model (GMM) based on the EM algorithm is used to estimate the probability density of the log data, which fits the probability distribution of the nonlinear multi-response log data. (ii) A framework combining active learning (AL) and semi-supervised learning is proposed for the expansion of labeled samples in the training data set. (iii) The application of pseudo-labeling detection technology can effectively improve the authenticity of pseudo-label samples. (iv) Different from the general deterministic lithology identification method, the result of the ALSLINB algorithm corresponds to the probability score, which provides an auxiliary basis for the prediction result. Finally, the ALSLINB algorithm is applied to two different data sets for a large number of experiments and compared with the related baseline methods to verify its effectiveness and generalization ability. The result proves that the ALSLINB algorithm can complete the lithology recognition task well and has high accuracy and robustness, which provides a new direction for intelligent lithology identification.

A Novel Method of Multitype Hybrid Rock Lithology Classification Based on Convolutional Neural Networks.

Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform.

Study on intelligent prediction method of rock drillability based on Bayesian lithology classification and optimized BP neural network

With the rapid development of shale gas in northeastern Sichuan province, problems such as low drilling rate, long drilling cycle and serious bit wear are becoming more and more prominent in shale gas drilling process. This paper proposes new prediction theory with two steps based on logging data sensitive to lithology by introducing Bayesian classifier and Self Adaptive Double Chain Quantum Genetic Algorithm (S-DC-QGA) which could optimize BP neural network prediction model. In the first step, the Bayesian network classifier achieved the classification and recognition of lithology, improved the correlation of model sample data, and enhanced the accuracy and speed of lithology classification. In the second step, S-DC-QGA characterized as the global optimization is used to optimize the number of hidden layer nodes, weights and thresholds of the BP neural network. Results showed that Bayesian lithologies classification enhanced the correlation of samples, moreover optimized BP neural network solved overfitting, random initial weights of BP itself and oscillation of fitting and generalization ability with slight change of network parameters, improving the prediction accuracy of rock drillability parameters and the generalization ability of the model. The findings of this study can help for better drilling design and improvement of mechanical drilling speed.

Simultaneous determination of lithology and major elements in rocks using laser-induced breakdown spectroscopy (LIBS) coupled with a deep convolutional neural network

By combining a convolutional neural network (CNN) and laser induced breakdown spectroscopy, this study reports a multi-task analysis method to simultaneously solve the problems of lithological recognition and the quantitative analysis of seven major chemical elements in rock analysis.

Prestack Bayesian lithofacies classification technology and application in oil-sand reservoir prediction

The oil-sand reservoirs in the Athabasca region of Canada are estuarine deposits affected by tides. The strata are inclined, and the interlayers are well-developed. Accurate spatial characterization of reservoirs and interlayers is the key for efficient oil-sand development. In this paper, we have used prestack Bayesian lithofacies classification technology to predict the spatial distribution characteristics of reservoirs and interlayers of oil-sand reservoirs. We first use log lithofacies data as a label, select lithofacies sensitive elastic parameters to make a lithofacies classification probability distribution crossplot, and then project the lithofacies-sensitive elastic parameter volumes into the lithofacies classification probability distribution crossplot. Finally, we predict the spatial probability distribution of different lithofacies. Probabilistic characterization can enhance the recognition of transitional lithology and thin layers in the inversion results, reduce the uncertainty in the prediction of reservoirs and interlayers, and significantly improve the prediction accuracy of reservoirs and interlayers. The field application results in the Kinosis study area indicate that the probability volume predicted by this technology can distinguish interlayers greater than 1 m thick and identify interlayers greater than 2 m thick, which meets the technical requirements of oil-sand steam-assisted gravity drainage development.

Research on lithology identification method based on mechanical specific energy principle and machine learning theory

Lithology identification is an important part of petroleum drilling engineering. Accurate identification of lithology is the foundation to ensure the smooth operation of drilling engineering. Conventional lithology recognition mainly relies on human experience. The recognition accuracy depends on the level of technical personnel and the recognition response time is lagging. It is difficult to meet the demand. How to achieve rapid and intelligent recognition of lithology is one of the core technical problems faced by oil drilling. In order to solve this problem, this paper uses the mechanical specific energy and other ground parameters to establish a rapid and intelligent recognition method of lithology based on the simulated annealing optimization support vector machine model. In order to improve the accuracy of the model recognition, a large number of methods have been developed from two classes and multiple classes, simulation analysis results show that the lithology recognition model based on the principle of mechanical specific energy and the simulated annealing optimization support vector machine algorithm can predict a priori unknown data with a prediction accuracy of over 90%. Compared with the support vector machine model and the K-means model, simulated annealing optimization support vector machine is used for comparative analysis, the algorithm to establish a lithology recognition model has better performance and higher accuracy. The intelligent identification model of lithology based on the principle of mechanical specific energy and simulated annealing optimization support vector machine algorithm established in this paper can quickly and accurately identify lithology, and provide new technical support for oil drilling formation analysis.

Lithology recognition of complex carbonatite by deep learning

The formation of carbonate reservoirs is affected by many factors such as sedimentation, diagenesis, and tectonic evolution. The rock composition and pore structures are complex, which brings challenges to the interpretation of reservoir lithology. Therefore, a novel approach of one-dimensional convolutional neural network architecture (1DCNN) based on the optimization of gradient descent algorithm for lithology identification is proposed. By fully combining logging physical parameters and vertical structure sequence context information, the deeper intrinsic rules between reservoir lithology and wireline logs can be found. With feature extracting of multi-scale deep feature extraction from multivariable wireline logs, the ability of wireline logs to express lithological features is improved, and the accuracy of lithological identification is further improved. In order to illustrate the prediction effect, the carbonate reservoir of the Majiagou Formation in Block 41-33 in Sulige Gas Field is taken as an example. The results reveal that 1DCNN improves the accuracy of carbonate lithology identification by 0.95% to 11.16%, which provides a new idea for the recognition of complex carbonate lithology.

Application of Artificial Intelligence in Lithology Recognition of Petroleum Logging in Low Permeability Reservoirs

In low permeability reservoirs, the conversion accuracy of the existing petroleum logging lithology identification method to small pore capillary pressure curve is not high, resulting in a low rock mass identification accuracy. Therefore, artificial intelligence technology is considered in this study to enhance the accuracy of lithology identification in low permeability reservoirs. Firstly, the radar mapping program is used to predict the position of reservoir oil logging, and then the small pore capillary pressure curve is converted by using the conversion method of piecewise power function scale to obtain the pore characteristics of low-permeability reservoir rocks. On this basis, the crossplot method is used to gather the pore characteristic data in well logging and form a plan, and the response parameters of well logging rock mass are obtained to realize the identification and analysis of lithology. The experimental results show that, compared with the existing identification methods, the accuracy of lithology identification in low-permeability reservoir logging is significantly increased after the application of artificial intelligence technology, and the identification process takes less time, which fully proves that the application of artificial intelligence technology is conducive to improving the performance of lithology identification.