Numerical Reservoir Modelling of the Leyte Geothermal Production Field, Philippines

Abstract

Philippines, a country in Southeast Asia, has an abundant resource of high-temperature geothermal systems applicable for electricity production. The country's current energy plan focuses on increasing the contribution of renewable resources to meet its power requirement and climate change policy. The Leyte Island in the Philippines hosts the Leyte Geothermal Production Field (LGPF), which has the largest total installed geothermal capacity. Geothermal reservoir assessment and resource management are some of the key factors in LGPF's sustainable operation for 36 years. A standard practice in the industry in developing and managing geothermal fields is reservoir modelling. The numerical model is used to characterize and understand the resource's behaviour to sustain supply and mitigate any effects on the environment. Reservoir modelling is an established approach in the LGPF to understand the evolving behaviour of its two reservoirs, namely, Tongonan and Mahanagdong systems. This project aimed to develop an improved numerical model of LGPF that incorporated new production data in 2010-2019 from the existing and new geothermal wells. The modelling process was carried out using the numerical simulator AUTOUGH2 (a variant of TOUGH2). It was aided by the computer programs TIM, Python, PYTOUGH, Grapher, and SIMGWEL. There were also model set-ups for some initial attempts of automatic parameter estimation in PEST and BeoPEST. The large number of data of the LGPF model, the time constraints on the project, and the COVID-19 pandemic were some of the challenges encountered in the study. However, acceptable matches between the modelled and the measured data were achieved during the natural state and the production history calibration stages. The natural state model represented the two high-temperature upflow reservoirs of Tongonan and Mahanagdong, and the natural two-phase region in the shallow portion of the Tongonan reservoir. The production history model reflected the significant changes in the pressure and enthalpy field data over time. The forecast model of the field up to 2030 showed that the reservoir can continue to support the current levels of production. More production data for new wells can improve the model and field data matches in a future modelling project.