Deep Learning for Geothermal Reservoir Characterization: Estimating Rock Properties from Seismic Data Using Convolutional Neural Networks
Bibliographic record
Abstract
Abstract Estimating rock properties is a crucial aspect of geothermal reservoir characterization, which plays a pivotal role in the efficient harnessing of geothermal energy. Rock properties include hydraulic properties, such as porosity and permeability, and elastic properties such as Poisson’s ratio, P-wave, S-wave velocity, bulk modulus, and acoustic impedance. Accurate determination of these properties allows geoscientists and reservoir engineers to assess and optimize the reservoir performance and assess the long-term stability of geothermal projects. Seismic inversion is the process of deriving these rock properties from seismic data. Conventional seismic inversion can be time-consuming and costly. Machine learning can effectively estimate rock properties which reducesthe need to rely on conventional seismic inversion, expensive lab experiments, and well logging data. This study aims to estimate keyrock properties (acoustic impedance, bulk modulus, density, permeability, Poisson’s ratio, and porosity) from the SCAN dataset using a convolutional neural network. The proposed U-net architecturewas used to develop models that rely on a full-stack seismic dataset as inputs to the model. Mean SquaredError (MSE) with a regularization factor was considered as a loss function when training the model and Mean Absolute Error (MAE) to assess the performance of the model. Results reveal an effective performance of the developed models in the estimation of rock properties with low MAE values ranging between 0.5-3 %. The higher MAE observed for the porosity and permeability estimation is attributed to poor data coverage in the ground truth data.This study demonstrates the potential of convolutional neural networks to predict rock properties from seismic data for efficient reservoir characterization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".