In the Middle East, there remain many technical challenges in the water saturation evaluation of carbonate rocks and the effective identification of reservoir fluid properties. The traditional Archie equation is not applicable to carbonate reservoirs with complex pore structures and varying reservoir space distribution, as there are obvious “non-Archie” phenomena. In this paper, by analyzing the experimental data on the rock resistivity of the target formation in the study area and analyzing the relationship between stratigraphic factors and porosity, the previous fitting method was modified as a result of using the actual data while avoiding the cementation index as a way to improve Archie’s formula to evaluate the water saturation. Based on the improved Archie formula, the mathematical differential operation of water saturation and porosity was carried out using the formation resistivity. The calculation results of irreducible water saturation were used to calibrate the oil layer, and the water layer was calibrated when the water saturation was 100%, allowing for a novel reservoir fluid property identification method. This total differential method can effectively identify the oil-down-to (ODT) and water-up-to (WUT) levels in an oil–water system and then accurately divide the transition zone of the oil–water layer. When this method was applied, the identification results were in good agreement with production conclusions and test data with an accuracy rate of 89.95%. Although the use of geophysical logging data from open-hole wells combined with the total differential method is only applicable to wells with similar logging time and production time, it is possible to compare geophysical logging data from different periods to construct oil–water profiles to observe the changes in ODT over time to guide development and adjust production plans. The proposed reservoir fluid property identification method and the improved water saturation calculation formula can meet the requirements of water saturation evaluation in the target block with low calculation cost and easy implementation, which provides a new method for water saturation evaluation and rapid identification of reservoir fluid properties.

Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved model-building efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.

Accurate evaluation of coalbed methane (CBM) content plays a momentous role in the identification and efficient development of favorable exploitation blocks of CBM resources, but there are still many technical challenges in the exploration and development of onshore CBM fields. With the development and application of geophysical logging technology, using geophysical logging data to predict the gas content of CBM reservoirs has been proven to be an effective and feasible solution. However, the complex logging response of the CBM reservoirs makes it difficult to characterize the relationship between the gas content and the logging curve response by a simple linear relationship. In this paper, kernel extreme learning machine (KELM), a machine learning method, is combined with the geophysical logging data to predict the vertical variation curve of gas content in CBM wells. In this paper, the laboratory data on coal rock gas content from 12 CBM wells in the Southern Shizhuang block are selected, and a CBM content prediction model based on the KELM method is constructed by selecting the log curves, combining cross-validation and grid-seeking to determine the hyperparameters, and validating the prediction model using the test dataset and a new well in the same block. The application of the model on the test dataset was remarkable, and the vertical variation of CBM content obtained by applying it to the new well was consistent with the laboratory results, which proved the correctness and generalizability of the model. The results of this paper show that the CBM content evaluation model based on the KELM method and geophysical logging data is applicable to the 3^# coal seam in the target block and can be used to predict the vertical CBM content of CBM wells; compared with the extreme learning machine (ELM) method and the backpropagation neural network (BPNN) method, the KELM method requires fewer hyperparameters to be explored when constructing the CBM content evaluation model, and the model construction is simple and has high prediction accuracy. At the same time, the CBM content model constructed by the KELM method differs for different blocks, coal seams at different depths, and different response ranges of geophysical logging data. The construction of a CBM content prediction model using the KELM method and logging curves is an effective means of characterizing CBM resources, and the model construction process and evaluation criteria studied in this paper can be used to help other blocks evaluate the CBM content, providing guidance for further exploration and development of CBM fields with practical application.

The content of industrial components of coalbeds, one of the main parameters of coalbed methane (CBM) reservoirs, is crucial in the entire coal mine resource exploration and exploitation process. Currently, using geophysical logging data to determine the content of industrial components is the most widely implemented method. In this study, the PZ block in the Qinshui Basin was employed as a target block to evaluate ash (A_ad), fixed carbon (FC_ad), volatile matter (V_daf), and moisture (M_ad) under the air-dry (AD) base condition based on the autocorrelation between the geophysical logging curves and industrial component contents combined with the OBGM (1, N) model. The results indicate that 1) the geophysical logging curves combined with the OBGM (1, N) model can accurately predict the A_ad and FC_ad contents and an increase in geophysical logging curve types can effectively improve the model performance, compared to using a single geophysical logging curve for prediction. 2) When predicting the V_daf content, using the geophysical logging curves combined with A_ad and FC_ad contents had the highest prediction accuracy. Further, prediction bias does not exist, compared to using only the geophysical logging curve or the autocorrelation between the industrial component contents. The entire evaluation process begins with an assessment of the A_ad and FC_ad contents. Then, the V_daf content was assessed using the content of these two industrial components combined with geophysical logging data. Finally, the M_ad content was calculated using the volumetric model. Accurate application results were obtained for the verification of new wells, demonstrating the efficacy of the method and procedure described in this study. 3) The OBGM (1, N) model has the highest prediction accuracy compared with the multiple regression and GM (0, N) models, which have the same computational cost. The geophysical logging interpretation model of the proposed coalbed industrial component contents is simple to calculate and suitable for small samples, providing a new method for the evaluation process of industrial component contents.

Evaluating irreducible water saturation is crucial for estimating reservoir capacity and developing effective extraction strategies. Traditional methods for predicting irreducible water saturation are limited by their reliance on specific logging data, which affects accuracy and applicability. This study introduces a predictive method based on fractal theory and deep learning for assessing irreducible water saturation in complex carbonate reservoirs. Utilizing the Mishrif Formation of the Halfaya oilfield as a case study, a new evaluation model was developed using the nuclear magnetic resonance (NMR) fractal permeability model and validated with surface NMR and mercury injection capillary pressure (MICP) data. The relationship between the logarithm mean of the transverse relaxation time (T_2lm) and physical properties was explored through fractal theory and the Thomeer Function. This relationship was integrated with conventional logging curves and an advanced deep learning algorithm to construct a T_2lm prediction model, offering a robust data foundation for irreducible water saturation evaluation. The results show that the new method is applicable to wells with and without specialized NMR logging data. For the Mishrif Formation, the predicted irreducible water saturation achieved a coefficient of determination of 0.943 compared to core results, with a mean absolute error of 2.37% and a mean relative error of 8.46%. Despite introducing additional errors with inverted T_2lm curves, it remains within acceptable limits. Compared to traditional methods, this approach provides enhanced predictive accuracy and broader applicability.