Sequentially optimized data acquisition for a geothermal reservoir

Corso, Anthony; Chiotoroiu, Maria; Clemens, Torsten; Zechner, Markus; Kochenderfer, Mykel J.

doi:10.1016/j.geothermics.2024.102983

Cited by 1 publication

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The numerical models consist of approximately 0.82 million cells and cover an area of 8 km from south to north and 6 km from east to west. The average reservoir thickness is approximately 200 m, with the uppermost layer of the reservoir at a depth of 1500 m in the northern region, gradually deepening to 3800 m in the south [70]. The porosity model is governed by well log interpretations, whereas the porosity and permeability distribution is given by geostatistical methods in areas away from the wells.…”

Section: Geological Reservoir Parameterisationmentioning

confidence: 99%

“…Corso et al [70] introduced a POMDP agent designed to address challenges in data acquisition. Below, the POMDP and agent as developed by Corso et al [70] is briefly described.…”

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%

“…The optimal policy π* is one that maximizes the expected discounted sum of future rewards. The components of the POMDP for the geothermal data acquisition case are as follows [70]:…”

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%

“…The solver underwent a single adjustment, allowing actions to have variable durations in computing the discount factor. Details of the solver parameters utilized can be found in Table 4 [70]. Corso et al [70] used various methods to determine the near optimal policy.…”

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%

“…Details of the solver parameters utilized can be found in Table 4 [70]. Corso et al [70] used various methods to determine the near optimal policy. Here, we cover the offline solver successive approximations of the reachable space under optimal policies (SARSOP) [78] and the "Acquire all" approach used by Corso et al [70].…”

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%

See 4 more Smart Citations

Artificial Intelligence-Centric Low-Enthalpy Geothermal Field Development Planning

Clemens,

Chiotoroiu,

Corso

et al. 2024

Energies

Self Cite

View full text Add to dashboard Cite

Low-enthalpy geothermal energy can make a major contribution towards reducing CO2 emissions. However, the development of geothermal reservoirs is costly and time intensive. In particular, high capital expenditures, data acquisition costs, and long periods of time from identifying a geothermal resource to geothermal heat extraction make geothermal field developments challenging. Conventional geothermal field development planning follows a linear approach starting with numerical model calibrations of the existing subsurface data, simulations of forecasts for geothermal heat production, and cost estimations. Next, data acquisition actions are evaluated and performed, and then the models are changed by integrating the new data before being finally used for forecasting and economics. There are several challenges when using this approach and the duration of model rebuilding with the availability of new data is time consuming. Furthermore, the approach does not address sequential decision making under uncertainty as it focuses on individual data acquisition actions. An artificial intelligence (AI)-centric approach to field development planning substantially improves cycle times and the expected rewards from geothermal projects. The reason for this is that various methods such as machine learning in data conditioning and distance-based generalized sensitivity analysis assess the uncertainty and quantify its potential impact on the final value. The use of AI for sequential decision making under uncertainty results in an optimized data acquisition strategy, a recommendation of a specific development scenario, or advice against further investment. This approach is illustrated by applying AI-centric geothermal field development planning to an Austrian low-enthalpy geothermal case. The results show an increase in the expected value of over 27% and a reduction in data acquisition costs by more than 35% when compared with conventional field development planning strategies. Furthermore, the results are used in systematic trade-off assessments of various key performance indicators.

show abstract

Section: Geological Reservoir Parameterisationmentioning

confidence: 99%

“…Corso et al [70] introduced a POMDP agent designed to address challenges in data acquisition. Below, the POMDP and agent as developed by Corso et al [70] is briefly described.…”

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%

“…The optimal policy π* is one that maximizes the expected discounted sum of future rewards. The components of the POMDP for the geothermal data acquisition case are as follows [70]:…”

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%

Section: Introduction Of Pomdp and Agentmentioning

confidence: 99%