Applications of artificial intelligence in geothermal resource exploration: A review

Artificial intelligence (AI) has become increasingly important in geothermal exploration,significantly improving the efficiency of resource identification.This review examines current AI applications,focusing on the algorithms used,the challenges addressed,and the opportunities created.In addition,the review highlights the growth of machine learning applications in geothermal exploration over the past decade,demonstrating how AI has improved the analysis of subsurface data to identify potential resources.AI techniques such as neural networks,support vector machines,and decision trees are used to estimate subsurface temperatures,predict rock and fluid properties,and identify optimal drilling locations.In particular,neural networks are the most widely used technique,further contributing to improved exploration efficiency.However,the widespread adoption of AI in geothermal exploration is hindered by challenges,such as data accessibility,data quality,and the need for tailored data science training for industry professionals.Furthermore,the review emphasizes the importance of data engineering methodologies,data scaling,and standardization to enable the development of accurate and generalizable AI models for geothermal exploration.It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources.By effectively addressing key challenges and leveraging AI technologies,the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

Fuzzy logic applied to value of information assessment in oil and gas projects

The concept of value of information(VOI)has been widely used in the oil industry when making decisions on the acquisition of new data sets for the development and operation of oil fields.The classical approach to VOI assumes that the outcome of the data acquisition process produces crisp values,which are uniquely mapped onto one of the deterministic reservoir models representing the subsurface variability.However,subsurface reservoir data are not always crisp;it can also be fuzzy and may correspond to various reservoir models to different degrees.The classical approach to VOI may not,therefore,lead to the best decision with regard to the need to acquire new data.Fuzzy logic,introduced in the 1960 s as an alternative to the classical logic,is able to manage the uncertainty associated with the fuzziness of the data.In this paper,both classical and fuzzy theoretical formulations for VOI are developed and contrasted using inherently vague data.A case study,which is consistent with the future development of an oil reservoir,is used to compare the application of both approaches to the estimation of VOI.The results of the VOI process show that when the fuzzy nature of the data is included in the assessment,the value of the data decreases.In this case study,the results of the assessment using crisp data and fuzzy data change the decision from"acquire"the additional data(in the former)to"do not acquire"the additional data(in the latter).In general,different decisions are reached,depending on whether the fuzzy nature of the data is considered during the evaluation.The implications of these results are significant in a domain such as the oil and gas industry(where investments are huge).This work strongly suggests the need to define the data as crisp or fuzzy for use in VOI,prior to implementing the assessment to select and define the right approach.