Exploration
Peyman Afzal; Sina Samadi; Mehran Arian; Ali Solgi; Zahra Maleki; Mohammad Seraj
Abstract
An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The ...
Read More
An important work for fractured reservoir modeling and development of oilfields is the delineation of geomechanical attributes such as permeability. The main aim of this research work is detection of permeability zones in the Asmari reservoir of Gachsaran oilfield (SW Iran) based on mud loss data. The mud loss was 3D estimated by ordinary kriging method. Then, fractal number-size, concentration-volume, and concentration-distance to fault models were applied for permeability zone classification. The concentration-distance to fault fractal model shows three permeability zones, and the concentration-volume fractal modeling represents eight zones with an index multifractal behavior. Moreover, the number-size fractal analysis presented that a multifractal behavior with five societies. The correlation between the results obtained by these fractal methods reveals that the obtained zones have a proper overlap together. High value permeability zones based on the concentration-distance to fault and concentration-volume fractal models are began from 501 Barrel Per Day (BPD) mud loss, and 630 BPD obtained by the N-S modeling. Fractal modeling indicates that the permeability zones occur in the SW, NW and southern parts of the Gachsaran oilfield which can be the fractured section of the Asmari reservoir rock. Main faults from this oilfield are correlated with the permeability zones derived via fractal modeling.
Exploration
Shaghayegh Esmaeilzadeh; Ali Moradzadeh; omid Asghari; Reza Mohebian
Abstract
Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings ...
Read More
Seismic inversion is a critical technique for estimating the spatial distribution of petro-elastic properties in the subsurface, based on the seismic reflection data. This work introduces an iterative geostatistical seismic inversion method, designed to address challenges in complex geological settings by incorporating self-updating local variogram models. Unlike the conventional approaches that rely on a single global variogram or fixed local variograms, the proposed method dynamically updates the spatial continuity models at each iteration using automatic variogram modeling and clustering of variogram parameters. The optimal number of clusters is determined using three cluster validity indices: Silhouette Index (SI), Davies-Bouldin Index (DB), and Calinski-Harabasz Index (CH). The method’s effectiveness was evaluated using a three-dimensional non-stationary synthetic dataset, demonstrating robust convergence when employing the SI and CH indices, with both achieving a high global correlation coefficient of 0.9 between the predicted and true seismic data. Among these, the CH index provided the best balance between the computational efficiency and inversion accuracy. The results highlight the method’s ability to effectively capture local spatial variability, while maintaining a reasonable computational cost, making it a promising approach for seismic inversion in complex sub-surface environments.
Exploration
Satyajeet Parida; Abhishek Kumar Tripathi; Tarek Salem Abdennaji; Yewuhalashet Fissha
Abstract
Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques ...
Read More
Coal quality is predominantly determined by its Gross Calorific Value (GCV), which directly influences its economic valuation. Traditional empirical formulas for GCV estimation, though effective, become inefficient and laborious when handling large datasets. To address this, machine learning (ML) techniques offer a robust alternative for accurate and rapid predictions. This study employs seven coal quality parameters. Total Moisture (TM), Ash (ASH), Volatile Matter (VM), Hydrogen (H), Carbon (C), Nitrogen (N), and Sulphur (S), as independent variables to develop predictive models for GCV. Four conventional regression techniques, namely Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT), along with two robust regression models Random Sample Consensus (RANSAC) and Huber Regressor (HR) are explored. The dataset comprises coal samples from five Asia-Pacific countries: China, Indonesia, Korea, the Philippines, and Thailand. Comparative performance analysis reveals that the robust regression models significantly outperform the conventional ML techniques. The RANSAC and Huber Regressor models achieve superior prediction accuracy with R² values of 0.9941 and 0.9952, respectively. These findings highlight the potential of robust regression approaches for reliable GCV estimation, facilitating efficient coal quality assessment in large-scale applications.
