Machine Learning Integration with Fuzzy Logic on Geothermal
Reservoir Monitoring – A Literature Review
1. Introduction to Reservoir Management/Monitoring and Fuzzy
Control
Management of production and reinjection operations is crucial for maintaining a generation plant
at full load. This multidisciplinary task involves production monitoring, geochemistry, reservoir
engineering, and simulation to combine surface and subsurface well data. Special geoscience tasks,
such as microgravity and ground deformation surveys, are also called on to understand reservoir
processes. A coherent model is created to predict future production and injection capacity and
identify areas for remedial action. Data may include wellhead pressure (WHP), valve with
reservoir monitoring data, and intermittent data like mass flow rate, enthalpy, and chemistry of
individual wells. Enthalpy changes indicate changes in the fluid feeding the well, such as
expansion or cooling, and can be accompanied by changes in fluid chemistry (Grant & Bixley,
2011). The main reason for monitoring the reservoir is to check for the decline in reservoir
properties – Pressure, Volume, and Temperature.
Re-injection planning involves placing injection wells as far from production wells as possible and
deep within the reservoir. In low-temperature fields, injections may be sited close in to provide
pressure support, but thermal degradation may occur. In high-temperature fields, pressure support
is usually not needed, but preventing thermal degradation is the main concern (Stefansson &
Steingrimsson, 1980). Designing injection-production layouts can be challenging, but these
constraints don't hinder development proposals.
Geothermal power systems require numerous production and reinjection wells to protect the
environment and feed geothermal reservoirs. Effective management is crucial for long-term energy
production and reservoir sustainability. Modern control systems only apply the expert's decision
at the moment. Geothermal systems are dynamic, and re-optimizing systems while avoiding
energy drops is complex (Haklidir, 2020). Fuzzy control application aims to minimize energy loss
by determining new optimization conditions quickly in reservoir conditions for geothermal field
production and reinjection well management. Fuzzy logic expresses human expertise, transferring
expertise from geothermal energy experts to fuzzy controllers (Haklidir, 2020).
Production and injection wells are crucial in optimizing geothermal resources, as they provide
characterization and monitoring of reservoirs. Machine learning has been used to predict wellbore
pressure drop and reservoir conditions, leading to data-driven reservoir management and
optimization. Studies have shown that neural networks and deep learning are the most commonly
applied techniques across all areas of the subsurface geothermal industry (Okoroafor, et al., 2022).
These techniques help predict the production of multilateral wells, reservoir characterization,
reservoir engineering, and production and injection well engineering. Understanding the behavior
of wells and forecasting reservoir conditions can lead to reduced downtime and operational costs
Fuzzy theory, developed by Lotfi A. Zadeh in 1965 (Zadeh, 1965), is used in various daily life
applications. It is particularly useful for designing control systems for complex non-linear
processes with uncertain initial conditions. Adaptive control, where the control law adapts to
changing dynamics, is used for non-linear dynamic processes with variable model parameters.
Fuzzy logic control (FLC) is a system that uses 'If then' rules to form a rule base. The multi-input
single-output (MISO) fuzzy rule base uses linguistic variables to take linguistic values from fuzzy
sets. The antecedent is a logical expression related to the input, while the consequent is related to
the output. Fuzzy Rule Inference (FRI) maps input and output space, and various control
techniques have been developed based on FRI (Dombi & Hussain, 2019).
2. Integration of Fuzzy Systems and Machine Learning
Fuzzy systems can be improved by incorporating machine learning techniques such as fuzzy
clustering, C-means, and hierarchical neuro-fuzzy inference intrusion detection systems (Prasad
& Shukla, 2021). These techniques are based on possibility theory and fuzzy set theory and can
handle linguistic, incomplete, imprecise, qualitative, noisy, or vague information. Fuzzy regression
methods are based on possibility theory and fuzzy set theory, with input and output parameters
being fuzzy. Researchers have developed models for convex optimization problems, fuzzy support
vector regression machines, and neural networks to deal with complex systems (Prasad & Shukla,
2021). Fuzzy rule-based expert systems are indeed used in various applications such as asthma
prediction, drug prescription, breast cancer classification, skin color diagnostics, cardiovascular
disease prediction, and more. These systems have also been used for measuring water quality,
anesthesia prediction, brain signal analysis, adaptive neuro-fuzzy inference systems (ANFIS), and
hybrid models for dust concentrations, susceptibility prediction, and air overpressure induced by
blasting.
Bibliography
Dombi, J., & Hussain, A. (2019). A New Approach to Fuzzy Control Using the Distending
Function. Journal of Process Control, 16-29.
Grant, M. A., & Bixley, P. F. (2011). Field Management. Oxford: Elsevier.
Haklidir, F. T. (2020). The Importance of Processing Geothermal Fluids for Sustainability of
Geothermal Power Plants: with Kizildere(Turkey) Geothermal Field Case. Science Direct.
Okoroafor, R. E., Smith, C., Ochie, K. I., Nwosu, J. C., Gudmundsdottir, H., & Mohammad, A.
(2022). Machine Learning in Subsurface Geothermal Energy: Two Decades in Review.
Geothermics, 102-401.
Prasad, R., & Shukla, P. K. (2021). A Review of the Hybridization of Fuzzy Systems and Machine
Learning Techniques. Computer Vision and Robotics (pp. 403 - 420). Springer Link.
Stefansson, V., & Steingrimsson, B. (1980). Production characteristics of wells tapping two-phase.
6th Workshop on Geothermal Reservoir Engineering (pp. 49 - 59). Stanford University.
Zadeh, L. A. (1965). Fuzzy sets, Information, and Control.