IMPROVED RESERVOIR CHARACTERIZATION AND SIMULATION OF A MATURE FIELD USING AN INTEGRATED APPROACH

Reservoir characterization involves various studies which comprises assimilation and interpretation of representative reservoir rock and fluid data for a simulation model under varying recovery mechanisms. The main challenge in reservoir simulation is the task of simplifying complex reservoir situations while ensuring a high level of data utilization to obtain a unique solution for history matching. Retaining geologic continuity in the simulation model is necessary to ensure the predictive capability of the reservoir model. In this study, the systematic assignment of reservoir properties with optimal utilization of very limited data has ensured that the fluid movement through the heterogeneous reservoir rock in a mature field is appropriately established. The key towards such a systematic assignment is classification of pore attributes. Pore attributes, which occur due to variations in depositional environments and diagenetic processes in a reservoir, have been identified through interpretation of the petrophysical data and the development of core- well log relationships in a consistent manner. Electrofacies along with petrophysical classification methods have been applied to quantify heterogeneity found in carbonate and sandstone reservoirs. It is observed that the electrofacies derived from well logs represent lithofacies found in the core measurements. The characterization approach has been shown to provide reliable accuracy of petrophysical property prediction when comparison was made with core measurements. These optimum correlation models were extended to uncored wells to describe the reservoir simulation model. A reservoir simulation model, built using this approach, provides a rapid means for history matching between the simulated results and the observed productions at the field while retaining the geological continuity. The integrated approach and structured methodology developed in this study resulted in a reservoir simulation model with adequate resolution of data that simulated the production history with sufficient realism, without necessity for alternations in petrophysical properties

Cervical Cancer Prediction using NGBFA Feature Selection Algorithm and Hybrid Ensemble Classifier

Cervical Cancer (CC) is a substantial reason of death midst middle-aged women throughout the world, specifically in developing countries, with approximately 85% of deaths. CC patients can be healed if spotted in the early stages. As no symptoms appear in the initial stages, it has become a challenge for investigators to predict the disease in the early stages. Several machine learning algorithms have been used to predict CC since the last decade. Instead of using a single classifier for the prediction, ensemble methods give accurate results, creating and combining multiple models to produce improved results. In this study, we built a hybrid ensemble classifier, 'A Robust Model Stacking: A Hybrid Ensemble,' in which a homogenous ensemble will be performed on a pool of classifiers in the base level followed by a heterogenous ensemble using the majority voting (soft) algorithm to get the final prediction of the new data. The dataset used in this study contains 858 instances with 32 features built from the risk factors and four targets made from the CC diagnosis tests. We have solved the data imbalance problem using an oversampling technique called SMOTE. The model's efficiency was evaluated based on the accuracy, recall, f1-score, precision, and AUC-ROC curve metrics for all four target variables in the dataset. The proposed Biopsy method's accuracy is 98%, Hinselmann is 97%, Schiller is 96.09%, and Citology is 93%. We implement ensemble learning in this study to increase prediction accuracy and decrease bias and variance. We carried the experiments out using the Python language in Google Colab and Jupyter notebooks. The experimental results revealed that our proposed hybrid ensemble learning records a remarkable accuracy for all four target variables

A Supervised Workflow for Predicting Lithofacies in Complex and Heterogeneous Tight Sandstone Reservoirs: A Data-Driven Approach Using Clustering and Classification Models

В этом исследовании представлен новый контролируемый техпроцесс (методика) для прогнозирования литофаций в сложных, неоднородных коллекторах из плотного песчаника с промежуточными фациями. Используя метод кластеризации с двумя информационными критериями, идентифицируются шесть различных фаций, что обеспечивает объективную, основанную на данных, альтернативу ручным подходам. Среди моделей классификации именно классификация гауссовских процессов (КГП) превосходит другие, в том числе машину опорных векторов (МОВ) и искусственную нейронную сеть (ИНС), при этом случайный лес (СЛ) работает менее эффективно. КГП точно предсказывает литофации в данных тестирования и оценивается на предмет точности сходства. Прогнозируемые литофации интегрируются в кросс-графики зависимости акустического импеданса от отношения скоростей, в результате чего получаются двумерные функции плотности вероятностей. В сочетании с данными о глубине они поступают в нейронную сеть для прогнозирования результатов синтетического гамма-каротажа. Результаты демонстрируют близкое согласование между измеренными и прогнозируемыми данными гамма-каротажа (R2 = 0,978) и почти идентичные тенденции (формы аномалий) каротажных диаграмм. Кроме того, прогнозируемые литофации классифицируются с использованием инвертированных объемов импеданса и отношения скоростей, что дает прогнозируемый объем фаций, который хорошо согласуется с классификацией литофаций скважины, даже без керновых данныхThis study introduces a novel supervised workflow for predicting lithofacies in complex, heterogeneous tight sandstone reservoirs with intercalated facies. Using a two-information criteria clustering method, six distinct facies are identified, providing an unbiased, data-driven alternative to manual approaches. Among classification models, Gaussian Process Classification (GPC) outperforms others, including Support Vector Machine (SVM) and Artificial Neural Network (ANN), with Random Forest (RF) performing less effectively. GPC accurately predicts lithofacies in testing data and is assessed for similarity accuracy. Predicted lithofacies are integrated into acoustic impedance versus velocity ratio cross plots, resulting in 2D probability density functions. These, combined with depth data, feed a neural network to forecast synthetic gamma-ray log responses. Results show strong agreement between measured and predicted gamma-ray logs (R2 = 0.978) and nearly identical log trends. Additionally, the predicted lithofacies are classified using inverted impedance and velocity ratio volumes, yielding a facies prediction volume that aligns well with well site lithofacies classification, even without core dat

Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China

The strong heterogeneity characteristics of deep-buried clastic low-permeability reservoirs may lead to great risks in hydrocarbon exploration and development, which makes the accurate identification of reservoir lithofacies crucial for improving the obtained exploration results. Due to the very limited core data acquired from deep drilling, lithofacies logging identification has become the most important method for comprehensively obtaining the rock information of deep-buried reservoirs and is a fundamental task for carrying out reservoir characterization and geological modeling. In this study, a machine learning method is introduced to lithofacies logging identification, to explore an accurate lithofacies identification method for deep fluvial-delta sandstone reservoirs with frequent lithofacies changes. Here Sangonghe Formation in the Central Junggar Basin of China is taken as an example. The K-means-based synthetic minority oversampling technique (K-means SMOTE) is employed to solve the problem regarding the imbalanced lithofacies data categories used to calibrate logging data, and a probabilistic calibration method is introduced to correct the likelihood function. To address the situation in which traditional machine learning methods ignore the geological deposition process, we introduce a depositional prior for controlling the vertical spreading process based on a Markov chain and propose an improved Bayesian inversion process for training on the log data to identify lithofacies. The results of a series of experiments show that, compared with the traditional machine learning method, the new method improves the recognition accuracy by 20%, and the predicted petrographic vertical distribution results are consistent with geological constraints. In addition, SMOTE and probabilistic calibration can effectively handle data imbalance problems so that different categories can be adequately learned. Also the introduction of geological prior has a positive impact on the overall distribution, which significantly improves the accuracy and recall rate of the method. According to this comprehensive analysis, the proposed method greatly enhanced the identification of the lithofacies distributions in the Sangonghe Formation. Therefore, this method can provide a tool for logging lithofacies interpretation of deep and strongly heterogeneous clastic reservoirs in fluvial-delta and other depositional environments

Reservoir Characterisation of Gas Shale through Sedimentary, Mineralogical, Petrophysical and Statistical Rock Types Evaluation

The successful exploration and production of the gas shale reservoirs can help to face the current energy crisis. However, shale is a fine-grained heterogeneous rock, so its exploration and development are challenging. This research has provided an integrated method for analysis, evaluation, and synthesis of potential gas shale formations in the Canning Basin, Western Australia. The results form a valuable case study that is applicable to many other sedimentary basins throughout the world

Multifractal analysis of the heterogeneity of nanopores in tight reservoirs based on boosting machine learning algorithms

Exploring the geological factors that affect fluid flow has always been a hot topic. For tight reservoirs, the pore structure and characteristics of different lithofacies reveal the storage status of fluids in different reservoir environments. The size, connectivity, and distribution of fillers in different sedimentary environments have always posed a challenge in studying the microscopic heterogeneity. In this paper, six logging curves (gamma-ray, density, acoustic, compensated neutron, shallow resistivity, and deep resistivity) in two marker wells, namely, J1 and J2, of the Permian Lucaogou Formation in the Jimsar Basin are tested by using four reinforcement learning algorithms: LogitBoost, GBM, XGBoost, and KNN. The total percent correct of training well J2 is 96%, 96%, 96%, and 96%, and the total percent correct of validation well J1 is 75%, 68%, 72%, and 75%, respectively. Based on the lithofacies classification obtained by using reinforcement learning algorithm, micropores, mesopores, and macropores are comprehensively described by high-pressure mercury injection and low-pressure nitrogen gas adsorption tests. The multifractal theory servers for the quantitative characterization of the pore distribution heterogeneity regarding different lithofacies samples, and as observed, the higher probability measure area of the generalized fractal spectrum affects the heterogeneity of the local interval of mesopores and macropores of the estuary dam. In the micropore and mesopore, the heterogeneity of the evaporation lake showed a large variation due to the influence of the higher probability measure area, and in the mesopore and macropore, the heterogeneity of the evaporation lake was controlled by the lower probability measure area. According to the correlation analysis, the single-fractal dimension is well related to the multifractal parameters, and the individual fitting degree reaches up to 99%, which can serve for characterizing the pore size distribution uniformity. The combination of boosting machine learning and multifractal can help to better characterize the micro-heterogeneity under different sedimentary environments and different pore size distribution ranges, which is helpful in the exploration and development of oil fields

Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest

Predicting the lithology, lithofacies and reservoir fluid classes of igneous rocks holds significant value in the domains of CO2 storage and reservoir evaluation. However, no precedent exists for research on the multi-label identification of igneous rocks. This study proposes a multi-label data augmented cascade forest method for the prediction of multilabel lithology, lithofacies and fluid using 9 conventional logging data features of cores collected from the eastern depression of the Liaohe Basin in northeastern China. Data augmentation is performed on an unbalanced multi-label training set using the multi-label synthetic minority over-sampling technique. Sample training is achieved by a multi-label cascade forest consisting of predictive clustering trees. These cascade structures possess adaptive feature selection and layer growth mechanisms. Given the necessity to focus on all possible outcomes and the generalization ability of the method, a simulated well model is built and then compared with 6 typical multi-label learning methods. The outperformance of this method in the evaluation metrics validates its superiority in terms of accuracy and generalization ability. The consistency of the predicted results and geological data of actual wells verifies the reliability of our method. Furthermore, the results show that it can be used as a reliable means of multi-label prediction of igneous lithology, lithofacies and reservoir fluids.Document Type: Original articleCited as: Han, R., Wang, Z., Guo, Y., Wang, X., A, R., Zhong, G. Multi-label prediction method for lithology, lithofacies and fluid classes based on data augmentation by cascade forest. Advances in Geo-Energy Research, 2023, 9(1): 25-37. https://doi.org/10.46690/ager.2023.07.0

Machine Learning Aided Production Data Analysis for Estimated Ultimate Recovery Forecasting

Estimated ultimate recovery (EUR) predictions are important in the petroleum industry. Many researchers have worked on implementing accurate EUR predictions. In this study, we used machine learning techniques to help predict the EUR range. We analyzed 200 Barnett shale wells with less than 170 months production history. We forecasted the production profile for each well using the modified Arps hyperbolic decline model. With the EUR values for 200 wells available, we forecasted the EUR of wells with limited production history by using three machine learning techniques, neural networks (NNet), support vector machine (SVM) and random forest (RF). The results show that the 200 sorted EUR values predicted with the commercial decline analysis software, ValNav, follows a lognormal distribution as indicated on a log-probability paper plot. The P90, P50 and P10 EUR values were identified and the low P10/P90 value of 2.3 shows a low variance of EUR in this geologic area. The production data were separated into eight groups and processed before being fed into the 3 machine learning algorithms. A four-fold cross-validation technique was employed to reduce the generalization error of the trained classifiers. The details of these 3 algorithms were also introduced. NNet performed best with highest test accuracy of 0.97 among the three machine learning algorithms employed with wells of 170 months’ production history. In addition, we also tested the EUR prediction performance with 24, 48, 96, and 170 months’ production history. The result shows that when we predict the wells’ EUR with increasing production history, we could achieve more accurate forecasting performance. The results in this project can be used to help oil and gas companies make financial decisions based on available production data in the same geologic area. Also, this project can also help provide a basis for researchers who are interested in this direction. Robustness analysis was implemented. The robustness of the algorithm is defined as the total distance of misclassified types to the correct types. Less total distance corresponds to more reliable and more stable performance for each individual algorithm. The NNet gives more robust performance with 100% misclassified samples classified into the types within one type distance to the correct types. RF is least robust. As the production history increases, the robustness of the three algorithms increases

Machine learning for the subsurface characterization at core, well, and reservoir scales

The development of machine learning techniques and the digitization of the subsurface geophysical/petrophysical measurements provides a new opportunity for the industries focusing on exploration and extraction of subsurface earth resources, such as oil, gas, coal, geothermal energy, mining, and sequestration. With more data and more computation power, the traditional methods for subsurface characterization and engineering that are adopted by these industries can be automized and improved. New phenomenon can be discovered, and new understandings may be acquired from the analysis of big data. The studies conducted in this dissertation explore the possibility of applying machine learning to improve the characterization of geological materials and geomaterials. Accurate characterization of subsurface hydrocarbon reservoirs is essential for economical oil and gas reservoir development. The characterization of reservoir formation requires the integration interpretation of data from different sources. Large-scale seismic measurements, intermediate-scale well logging measurements, and small-scale core sample measurements help engineers understand the characteristics of the hydrocarbon reservoirs. Seismic data acquisition is expensive and core samples are sparse and have limited volume. Consequently, well log acquisition provides essential information that improves seismic analysis and core analysis. However, the well logging data may be missing due to financial or operational challenges or may be contaminated due to complex downhole environment. At the near-wellbore scale, I solve the data constraint problem in the reservoir characterization by applying machine learning models to generate synthetic sonic traveltime and NMR logs that are crucial for geomechanical and pore-scale characterization, respectively. At the core scale, I solve the problems in fracture characterization by processing the multipoint sonic wave propagation measurements using machine learning to characterize the dispersion, orientation, and distribution of cracks embedded in material. At reservoir scale, I utilize reinforcement learning models to achieve automatic history matching by using a fast-marching-based reservoir simulator to estimate reservoir permeability that controls pressure transient response of the well. The application of machine learning provides new insights into traditional subsurface characterization techniques. First, by applying shallow and deep machine learning models, sonic logs and NMR T2 logs can be acquired from other easy-to-acquire well logs with high accuracy. Second, the development of the sonic wave propagation simulator enables the characterization of crack-bearing materials with the simple wavefront arrival times. Third, the combination of reinforcement learning algorithms and encapsulated reservoir simulation provides a possible solution for automatic history matching