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All Journal Journal Of Human And Education (JAHE) Scientific Contributions Oil and Gas
Ambia, Fajril
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Humanities Chemical Engineering, Chemistry & Bioengineering Education Energy Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Pendampingan Hulu-Hilir Pertanian Hortikultura di Desa Batu Belah: Afdhol, Muhammad Khairul; Erfando, Tomi; Ambia, Fajril; Aldio, Rieza Zulrian; Nordin, Razif Muhammed; Wicaksono, Agung; Saputri, Rika Lala; Sahputra, Rendi; Cardova, Rusdi Agil; Hamidah, Ghoty
Journal Of Human And Education (JAHE) Vol. 5 No. 2 (2025): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v5i2.2321

Abstract

Desa Batu Belah memiliki potensi besar dalam sektor hortikultura, namun petani menghadapi berbagai tantangan, termasuk ketergantungan pada pupuk kimia yang meningkatkan biaya produksi, sistem irigasi yang tidak efisien, serta keterbatasan akses pemasaran yang menyebabkan ketergantungan pada tengkulak. Tanpa intervensi, permasalahan ini dapat menghambat produktivitas dan kesejahteraan petani. Program ini bertujuan untuk meningkatkan kapasitas petani dalam mengoptimalkan produksi hortikultura melalui penerapan teknologi reaktor fermentasi untuk pupuk organik, edukasi sistem irigasi tetes guna meningkatkan efisiensi penggunaan air, serta pendampingan pemasaran digital untuk memperluas akses pasar. Metode yang digunakan mencakup sosialisasi kepada masyarakat, pelatihan intensif mengenai teknologi fermentasi limbah organik dan sistem irigasi hemat air, serta pendampingan pemasaran berbasis digital. Implementasi dilakukan melalui pendekatan partisipatif dengan melibatkan petani, akademisi, serta mitra strategis, termasuk Universitas Islam Riau dan UiTM Cawangan Perlis. Hasil program menunjukkan dampak signifikan, di antaranya: peningkatan produksi pupuk organik hingga 400 kg per batch dengan efisiensi biaya hingga 40%, pengurangan pemborosan air hingga 70% melalui irigasi tetes, serta kenaikan pendapatan petani sebesar 20% akibat akses pasar yang lebih luas melalui platform digital. Program ini tidak hanya meningkatkan produktivitas petani, tetapi juga menciptakan sistem pertanian yang lebih berkelanjutan dan mandiri.
The Integration of Hybrid Capacitance Resistance Model and Machine Learning: A Data-Based Workflow for Optimizing Waterflood Performance and Reservoir Management Muhendra, Syifa Alviola; Rita, Novia; Ambia, Fajril; Dahlia, Agus
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1928

Abstract

This study aims to minimize uncertainty in waterflood performance by employing a data-driven workflow that combines the Capacitance Resistance Model (CRM) with Machine Learning. Two CRM variants, CRM-P (Producer-based) and CRM-IP (Injector-Producer-based), are utilized to evaluate interwell connectivity and time constants on three reservoir models: homogeneous, heterogeneous, and a real field scenario (Volve Field). The model is evaluated using R² and Mean Absolute Percentage Error (MAPE) and is compared against the Random Forest and eXtreme Gradient Boosting (XGBoost) techniques. The results indicate that CRM-IP provides more realistic estimates than CRM-P, particularly for response time. XGBoost consistently demonstrates superior prediction accuracy, achieving R² values of 0.76–0.98 and MAPE values of 0.5–10%. Three-dimensional (3D) visualizations of interwell connectivity and streamline analysis strengthen the understanding of fluid flow and sweep efficiency. This further demonstrates that integrating CRM and Machine Learning serves as a decision-support tool for Enhanced Oil Recovery optimization, as evidenced by R² and MAPE analyses that characterize sweep efficiency and the reservoir's capacity to accommodate additional injection.
Comparative Study of Capacitance Resistance Model and Machine Learning for Sensitivity Analysis of Polymer Injection Performance Rizal, Azri Agus; Ambia, Fajril; Rita, Novia; Herawati, Ira
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1929

Abstract

The objective of this study was to evaluate the performance of polymer injection in the Volve Field by validating full-physics tNavigator simulation results. This process was performed using two independent data-driven approaches: the Capacitance Resistance Model (CRM) and machine-learning algorithms Random Forest and XGBoost. This validation framework addresses uncertainty in flow-parameter and ensures that simulated production responses align with data-driven injection–production behavior. The simulation model was constructed using 20 years of historical field data, consisted of five years of polymer injection at 1000–3000 ppm, followed by 15 years of chase water flooding. The simulation results showed that polymer injection increased the oil recovery factor from 21.12% to 21.30% in the best-case scenario, indicating a modest improvement in sweep efficiency. CRM, applied through CRM-P and CRM-IP configurations, successfully reconstructed production profiles and quantified interwell connectivity (R² = 0.94; MAPE < 10%). Machine-learning validation further confirmed these results, with Random Forest achieving R² = 0.92 (MAPE < 1%) and XGBoost achieving R² = 0.99 (MAPE < 1%). Overall, CRM and machine learning provide effective and independent validation pathways, enhancing confidence in simulation outcomes and allowing for reliable assessment of polymer-injection performance in field applications.
Comparative Analysis of Capacitance-Resistance Models and Machine Learning for Co₂-Eor Production Forecasting: A Case Study of Dynamic Connectivity in Heterogeneous Reservoir Rafsanjani, Reyhan; Dahlia, Agus; Ambia, Fajril; Rita, Novia; Husbani, Ayyi
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1930

Abstract

This study evaluates an integrated forecasting framework that combines Capacitance-Resistance Models (CRMP and CRMIP) with ensemble machine learning algorithms (Random Forest and XGBoost) to predict CO₂-Enhanced Oil Recovery performance in the heterogeneous Volve Field. Reservoir simulation was performed using tNavigator with CO₂ injection at 941 tons/day (35 MMSCF/day) over 20 years. The results demonstrate the critical influence of CO₂-specific characteristics, with a determined Minimum Miscibility Pressure of 3299.68 psi and a corresponding oil Swelling Factor of 1.19. Machine learning models, particularly XGBoost, significantly outperformed conventional CRM methods, achieving exceptional accuracy (R² = 0.99-1.00, MAPE = 0.44-2.24%) compared to CRMP/CRMIP (R² = 0.55-0.72, MAPE = 16-23%). The CO₂ injection scenario substantially enhanced oil recovery, achieving a cumulative production of 15.73 MMSTB (RF 20.45%) compared to 9.38 MMSTB (RF 12.19%) for waterflooding, representing a 67.7% improvement and incremental recovery of 6.35 MMSTB. Interwell connectivity analysis revealed dynamic reservoir responses with time constants ranging from 916 to 927 days. The integration of physics-based models with non-linear machine learning algorithms significantly improves prediction accuracy while providing comprehensive insights into reservoir dynamics, allowing for optimal CCUS implementation in heterogeneous reservoir systems.
Co-Authors Agung Wicaksono Agus Dahlia Cardova, Rusdi Agil Hamidah, Ghoty Husbani, Ayyi Ira Herawati, Ira Muhammad Khairul Afdhol Muhendra, Syifa Alviola Nordin, Razif Muhammed Novia Rita Rafsanjani, Reyhan Rizal, Azri Agus Sahputra, Rendi Saputri, Rika Lala Tomi Erfando, Tomi