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Event mining : algorithms and applications
\"Event mining encompasses techniques for automatically and efficiently extracting valuable knowledge from historical event/log data. The field, therefore, plays an important role in data-driven system management. Event Mining: Algorithms and Applications presents state-of-the-art event mining approaches and applications with a focus on computing system management. The book first explains how to transform log data in disparate formats and contents into a canonical form as well as how to optimize system monitoring. It then describes intelligent and efficient methods and algorithms to perform data-driven pattern discovery and problem determination for managing complex systems. The book also discusses data-driven approaches for the detailed diagnosis of a system issue and addresses the application of event summarization in Twitter messages (tweets). Understanding the interdisciplinary field of event mining can be challenging as it requires familiarity with several research areas and the relevant literature is scattered in diverse publications. This book makes it easier to explore the field by providing both a good starting point if you are not familiar with the topics and a comprehensive reference if you are already working in this area\"--Back cover.
Book
An integrated approach for the identification of lithofacies and clay mineralogy through Neuro-Fuzzy, cross plot, and statistical analyses, from well log data
Today, researchers face multiple challenges identifying clay mineral types and lithofacies from well log data. This research paper hopes to offer new insight into this particular challenge. Formation evaluation characteristics play a significant role in the exploration and production of future and current oil and gas fields. The proposed methodology in this study uses an integrated approach that includes: (1) numerical equations, (2) Neuro-Fuzzy neural networks, (3) cross plots, and (4) statistical analyses. This proposed integrated approach is capable of dramatically improving the accuracy of the results. Well logging data provide valuable information for identifying lithofacies, clay mineralogy types, as well as other important hydrocarbon reservoir characteristics. Talhar Shale in the Southern Lower Indus Basin, Pakistan, is composed of interbedded shale, sand, and shaly-sand, intervals that have been identified via the lithological interpretation process of well logs. Talhar Shale contains montmorillonite type clay with minor amounts of illite, glauconite, and various micas that can be easily identified by natural gamma ray absorption profiles, as well as through ratio logs, bulk density log, and photoelectric absorption index log. These interpretations can be further confirmed via cross plots and other statistical analyses. This approach consists of a comprehensive study of well logging data and thus can lend itself to be a helpful component in characterizing the hydrocarbon structures of the Talhar Shale.
Journal Article
A Novel Intelligent Sedimentary Microfacies Identification Model Based on Limited Well‐Logging Data
Sedimentary microfacies identification is fundamental for reservoir characterization, directly influencing hydrocarbon exploration and production strategies. However, traditional methods relying on core analysis, seismic interpretation, and manual well‐log analysis face significant challenges: (1) high costs and limited coverage of coring data, (2) subjectivity in seismic facies interpretation, and (3) poor generalization of conventional machine learning models when trained on small datasets. To overcome these limitations, this study proposes Hopular—a novel deep learning architecture leveraging modern Hopfield networks. We validated the framework using 4000 normalized data points from 10 wells, covering eight logging parameters and five microfacies types. Evaluations across small (≤ 500 samples), medium (≤ 2000), and large (≥ 3000) datasets demonstrated robust performance, with R 2 scores of 0.704 (±0.021), 0.809 (±0.059), and 0.925, respectively. The model excels in capturing data relationships, particularly in small data regimes (11.6% R 2 improvement over ensemble methods). In summary, Hopular provides an accurate, data‐efficient solution for microfacies identification and supports exploration in data‐scarce settings. This work advances reservoir characterization by combining Hopfield networks’ associative memory with deep learning, offering reliable technical support for subsurface interpretation.
Journal Article
Prediction of Oil Reservoir Porosity Using Petrophysical Data and a New Intelligent Hybrid Method
In hydrocarbon reserves, porosity is an important parameter that defines the volume and mobility of the porous fluid. Reservoir and management operations are greatly influenced by porosity. Usually, the standard methods for determining porosity are core analysis and well testing. These methods are very expensive, and generally wells in a field do not have a core. As a result, the methods that can determine the petrophysical properties of the reservoir, including porosity and well logging charts, are very important because well logs are usually available for all wells of a field. Artificial intelligence methods are new, low-cost and accurate methods that can indirectly estimate reservoir porosity in the shortest possible time using well-logging data. In this study, a new intelligent method of support vector regression with sparrow search algorithm (SVR-SSA) was used to indirectly estimate the porosity of a hydrocarbon reservoir in southwestern Iran (Azadegan oil field). Then, the performance of the hybrid model was compared to that of support vector regression (SVR). A total of 2506 well logging data were included in the database and were divided into two categories of training data (1754 data points) and test data (752 data points) for evaluating models. For the training data set of the SVR-SSA model, R2, mean squared error (MSE), and root mean squared error (RMSE) values were 0.98, 0.000933, and 0.030555, and those for the SVR model were 0.9072, 0.001096 and 0.033108, respectively. Also, for the SVR-SSA model test data set, R2, MSE, and RMSE values were 0.9726, 0.001032, and 0.032128 and those for the SVR model were 0.8931, 0.001660 and 0.040750, respectively. Comparing SVR-SSA and SVR based on R2, MSE and RMSE performance indicators revealed that SVR-SSA outperformed other models in predicting porosity. SVR-SSA is, therefore, a powerful, fast and accurate method of indirectly estimating porosity in reservoirs where porosity is not measured directly in the core.
Journal Article
Identification of reservoir types in deep carbonates based on mixed-kernel machine learning using geophysical logging data
Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces. Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency, which cannot accurately reflect the nonlinear relationship between reservoir types and logging data. Recently, the kernel Fisher discriminant analysis (KFD), a kernel-based machine learning technique, attracts attention in many fields because of its strong nonlinear processing ability. However, the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well, especially for highly complex data cases. To address this issue, in this study, a mixed kernel Fisher discriminant analysis (MKFD) model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin, China. The MKFD model was trained and tested with 453 datasets from 7 coring wells, utilizing GR, CAL, DEN, AC, CNL and RT logs as input variables. The particle swarm optimization (PSO) was adopted for hyper-parameter optimization of MKFD model. To evaluate the model performance, prediction results of MKFD were compared with those of basic-kernel based KFD, RF and SVM models. Subsequently, the built MKFD model was applied in a blind well test, and a variable importance analysis was conducted. The comparison and blind test results demonstrated that MKFD outperformed traditional KFD, RF and SVM in the identification of reservoir types, which provided higher accuracy and stronger generalization. The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.
Journal Article
STNet: Advancing Lithology Identification with a Spatiotemporal Deep Learning Framework for Well Logging Data
In the realm of oil and gas exploration, accurate identification of lithology is imperative for the assessment of resources and the refinement of extraction strategies. While artificial intelligence techniques have garnered considerable success in lithology identification, existing methodologies encounter difficulties when addressing highly heterogeneous and geologically intricate unconventional oil and gas reservoirs. Specifically, they struggle to account for the dynamic variations in sample characteristics across spatial dimensions and temporal sequences. This separate treatment of spatial and temporal dynamics not only confines the precision of fluid prediction but also significantly attenuates the robustness of the models. To address this challenge, we propose the spatiotemporal network (STNet), a dual-branch deep learning framework that integrates seamlessly spatial feature graph methods with time-sequential prediction methods. By employing a graph structure that accounts for spatial characteristics to capture the complex spatial relationships within logging data, and by utilizing a temporal model to discern the dynamic properties of time series data, this dual-mechanism framework enables a more comprehensive understanding of the multidimensional attributes of subsurface fluids, thereby enhancing the accuracy of lithology identification. Experimental results from multiple wells in different regions of the Tarim and Daqing oilfields demonstrate that STNet not only achieves detection accuracy exceeding 95% but also exhibits strong generalizability. The results indicate a significant improvement in the accuracy of lithology identification compared to seven other advanced models. Integrating both temporal and spatial elements of logging data provides a new perspective for enhancing fluid prediction capabilities.
Journal Article
Method for identifying post-fracturing productivity classification in tight gas reservoirs based on principal component analysis
The tight sandstone reservoirs in the Linxingdong area of the Ordos Basin are characterized by low porosity, high irreducible water saturation, low gas saturation, and strong heterogeneity, with conventional logging methods showing low accuracy in identifying reservoir fluid properties. This paper proposes a method based on principal component analysis (PCA) for identifying post-fracturing productivity classification in complex tight gas reservoirs. Initially, the fluid productivity classification type of the test layers was determined by integrating gas and liquid production data from the fracturing and production stages. Subsequently, logging data were utilized to select curves sensitive to reservoir fluid properties, and a calculation formula for the fluid property identification factor D was established based on PCA. The classification of reservoir fluid productivity in the study area was then identified based on the D value. The results indicate that the fluid property identification factor method based on PCA performed well in the study area, with an accuracy of 83%. Compared with other conventional methods, this approach fully exploits geophysical logging data, has strong generalizability, and provides data support for the formulation and adjustment of subsequent development plans.
Journal Article
Full-waveform inversion constrained by adaptive sonic logging data within a Bayesian framework
The full waveform inversion (FWI) utilizes full wavefield data to invert subsurface parameters and is considered one of the most promising data-driven tools for obtaining high precision velocity models. However, the successful application of FWI in geophysical exploration remains limited, primarily due to the cycle-skipping issue caused by the absence of low-frequency data, which is one of the main reasons for FWI failures. Incorporating prior regularization constraints FWI can effectively compensate for the lacking low-frequency components and constrain the iterative updates of FWI toward the desired direction, offering a natural advantage in addressing this challenge. However, the weights of the prior information terms are still determined empirically, which introduces significant subjectivity and randomness to the inversion results. To solve this issue, we propose an adaptive method to determine the weight factor based on posterior probability distribution within the Bayesian theoretical framework. This factor adaptively adjusts during each iteration to balance the contributions of the data error term and the prior information term in FWI, which can effectively mitigate the cycle-skipping problem and alleviating the nonlinearity of the inversion process. Numerical examples from the Overthrust model and the Marmousi model show that our method not only enhance the accuracy of FWI, but also demonstrate strong noise resistance.
Journal Article
Lithology identification technology of logging data based on deep learning model
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.
Journal Article
A novel approach for reconstructing paleo-overpressure through basin simulations constrained by logging data a case study of Tight gas in Sulige Gas field, Ordos Basin
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.
Journal Article