Geothermal Reservoir Identification based on Gravity Data Analysis in Rajabasa Area- Lampung

Gravity research in the Rajabasa geothermal prospect area was conducted to determine geothermalreservoirs and faults as reservoir boundaries. The research includes spectrum analysis and separation of the Bouguer anomaly to obtain a residual Bouguer anomaly, gradient analysis using the second vertical derivative (SVD) technique to identify fault structures or lithological contact, and 3D inversion modeling of the residual Bouguer anomaly to obtain a 3D density distribution subsurface model. Analysis was performed based on all results with supplementary data from geology, geochemistry, micro-earthquake (MEQ) epicenter distribution map, and magnetotelluric (MT) inversion profiles. The study found 3 (three) geothermal reservoirs in Mount Balirang, west of Mount Rajabasa, and south of Pangkul Hot Spring, with a depth of around 1,000-1,500 m from the ground level. Fault structures and lithologies separate the three reservoirs. The location of the reservoir in the Balirang mountain area corresponds to the model data from MEQ, temperature, and magnetotelluric resistivity data. The heat source of the geothermal system is under Mount Rajabasa, which is indicated by the presence of high-density values (might be frozen residual magma), high-temperature values, and the high number of micro-earthquakes epicenters below the peak of Mount Rajabasa.

Rock Thickness Identification by XGBoost with Logging Data

Based on research on the response mechanism of rock formations and reservoirs to logging curves, 12 logging curves selected by combining the depth characteristics of formations are proposed to identify rock formations and reservoirs using four algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF) and XGBoost. Out of 60 wells in the study block, 57 wells were selected for training and learning, and the remaining 3 wells were used as prediction samples for testing the algorithm. The recognition of rock formations and reservoirs is performed by each of these four machine learning algorithms, and predictive knowledge is obtained separately. It was found that the accuracy of the 4 algorithms for rock formation and reservoir layer identification reached over 90%, but the XGBoost algorithm was found to be the best in terms of the 4 scoring criteria of F1-score, precision, recall and accuracy. The accuracy of rock formation identification could reach over 95%, and the correlation analysis between the logging curve and rock formation could be performed on this basis. The results show that the RMN, RLLD and RLLS have the most obvious responses to the sandstone layer, off-surface reservoir and effective thickness layer, and the CAL has the least effect on the formation and reservoir identification, which can provide an effective reference for the selection and dimensionality reduction of the subsequent logging curves.

Numerical Simulation Study of Tracking the Displacement Fronts and Enhancing Oil Recovery Based on Ferrofluid Flooding

Water flooding is crucial means to improve oil recovery after primary production. However, the utilization ratio of injected water is often seriously affected by heterogeneities in the reservoir. Identification of the location of the displacement fronts and the associated reservoir heterogeneity is important for the management and improvement of water flooding. In recent years, ferrofluids have generated much interest from the oil industry owning to its unique properties. First, saturation of ferrofluids alters the magnetic permeability of the porous medium, which means that the presence of ferrofluids should produce magnetic anomalies in an externally imposed magnetic field or the local geomagnetic field. Second, with a strong external magnetic field, ferrofluids can be guided into regions that were bypassed and with high residual oil saturation. In view of these properties, a potential dual-application of ferrofluid as both a tracer to locate the displacement front and a displacing fluid to improve recovery in a heterogeneous reservoir is examined in this paper. Throughout the injection process, the magnetic field generated by electromagnets and altered by the distribution of ferrofluids was calculated dynamically by applying a finite element method, and a finite volume method was used to solve the multiphase flow. Numerical simulation results indicate that the displacement fronts in reservoirs can indeed be detected, through which the major features of reservoir heterogeneity can be inferred. After the locations of the displacement fronts and reservoir heterogeneities are identified, strong magnetic fields were applied to direct ferrofluids into poorly swept regions and the efficiency of the flooding was significantly improved.

Identification and logging evaluation of poor reservoirs in X Oilfield

The reservoirs of X Oilfield have the characteristics of fine lithology particles, strong pore structure heterogeneity, and high argillaceous reservoirs and thin layers are generally developed. Conventional logging interpretation cannot make a fine evaluation, which results in serious discrepancies between the interpretation results of some reservoirs and actual production performance, and reserves are underestimated. Improving poor reservoir identification and logging evaluation accuracy is of great significance to oilfield development. The flow zone indicator (FZI) is used to classify the reservoirs into three types, I, II, and III, and the classification results are combined to establish a reservoir type identification chart based on logging curves; the resolution matching method and the deconvolution method are used to improve the accuracy of thin-layer recognition. Finally, the logging interpretation model is reestablished. Logging evaluations were conducted on 20 wells in X oilfield, and Y core wells were used for verification. The application results show that this method can effectively improve the identification accuracy of thin oilfields and high argillaceous reservoirs; the results of fine logging interpretation of poor reservoirs are consistent with core analysis conclusions and actual production conditions, which are typical of the successful application of poor reservoir technology.