TY  - JOUR
AU  - Brantson, Eric Thompson
AU  - Obeng, Stephen Dwamena Akoto
AU  - Ocran, Daniel
AU  - Duodu, Emmanuel Karikari
AU  - Iyiola, Zainab Ololade
AU  - Adjei, Eugene Jerry
AU  - Adu-Awuku, Joel
AU  - Owusu, Adu Gyamfi
PY  - 2026
DA  - 2026/04/30
TI  - Evaluating Enthalpy Production in Geothermal Reservoirs: Insights from Response Surface Methodology and Advanced Machine Learning Techniques
JO  - Reservoir Science
T2  - Reservoir Science
JF  - Reservoir Science
VL  - 2
IS  - 2
SP  - 151
EP  - 171
DO  - 10.62762/RS.2026.366192
UR  - https://www.icck.org/article/abs/RS.2026.366192
KW  - geothermal reservoir
KW  - response surface methodology
KW  - enthalpy prediction
KW  - particle swarm optimization
KW  - enhanced geothermal system
AB  - Enthalpy is a key thermodynamic parameter governing energy extraction efficiency in Enhanced Geothermal Systems (EGS). Although Machine Learning (ML) has been widely applied in geothermal modeling, few studies have systematically integrated Response Surface Methodology (RSM) with ML to develop and compare multiple predictive models for enthalpy production. Using datasets from CMG STARS simulations, we developed predictive models based on RSM and four ML techniques (Random Forest, Decision Tree, XGBoost, and Support Vector Machine). A Central Composite Design (CCD) in CMG CMOST established relationships between operational parameters and enthalpy, while Particle Swarm Optimization (PSO) determined the optimal operating conditions within the investigated design space. The RSM model achieved an \(R^2\) of 0.999404, outperforming Random Forest (0.9929), Decision Tree (0.9882), XGBoost (0.9883), and Support Vector Machine (0.9888). PSO optimization yielded a maximum cumulative enthalpy of \(4.507 \times 10^{16}\) J. These results demonstrate that integrating RSM with ML provides accurate and robust predictive capability with efficient optimization of geothermal reservoir performance. This study offers a structured framework combining statistical and data-driven approaches to improve geothermal energy assessment and decision-making.
SN  - 3070-2356
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Brantson2026Evaluating,
  author = {Eric Thompson Brantson and Stephen Dwamena Akoto Obeng and Daniel Ocran and Emmanuel Karikari Duodu and Zainab Ololade Iyiola and Eugene Jerry Adjei and Joel Adu-Awuku and Adu Gyamfi Owusu},
  title = {Evaluating Enthalpy Production in Geothermal Reservoirs: Insights from Response Surface Methodology and Advanced Machine Learning Techniques},
  journal = {Reservoir Science},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {151-171},
  doi = {10.62762/RS.2026.366192},
  url = {https://www.icck.org/article/abs/RS.2026.366192},
  abstract = {Enthalpy is a key thermodynamic parameter governing energy extraction efficiency in Enhanced Geothermal Systems (EGS). Although Machine Learning (ML) has been widely applied in geothermal modeling, few studies have systematically integrated Response Surface Methodology (RSM) with ML to develop and compare multiple predictive models for enthalpy production. Using datasets from CMG STARS simulations, we developed predictive models based on RSM and four ML techniques (Random Forest, Decision Tree, XGBoost, and Support Vector Machine). A Central Composite Design (CCD) in CMG CMOST established relationships between operational parameters and enthalpy, while Particle Swarm Optimization (PSO) determined the optimal operating conditions within the investigated design space. The RSM model achieved an \(R^2\) of 0.999404, outperforming Random Forest (0.9929), Decision Tree (0.9882), XGBoost (0.9883), and Support Vector Machine (0.9888). PSO optimization yielded a maximum cumulative enthalpy of \(4.507 \times 10^{16}\) J. These results demonstrate that integrating RSM with ML provides accurate and robust predictive capability with efficient optimization of geothermal reservoir performance. This study offers a structured framework combining statistical and data-driven approaches to improve geothermal energy assessment and decision-making.},
  keywords = {geothermal reservoir, response surface methodology, enthalpy prediction, particle swarm optimization, enhanced geothermal system},
  issn = {3070-2356},
  publisher = {Institute of Central Computation and Knowledge}
}