Petrophysical Open Hole Log Evaluation Using Artificial Intelligence in a Sandstone Gas Field Offshore UK
Bibliographic record
Abstract
Abstract In the context of a Carbon Capture and Storage project requiring the petrophysical re-evaluation of approximately 200 well penetrations in a mature offshore UK giant gas field, we assessed the suitability of artificial intelligence (AI) to predict volume of clay, porosity and water saturation. The reservoir is a relatively homogeneous gas-bearing sandstone with porosity varying from 10 to 15% and low clay content. The selected AI model was a supervised learning approach (eXtreme Gradient Boosting algorithm), with Root Mean Square Error (RMSE) used as a model performance indicator. Dataset cleaning and preparation, including selecting the input logs and wells, was paramount to model performance. In the study, 181 wells were used, as they presented at least 3 open hole logs in the reservoir section, Gamma Ray, Density and Resistivity, to be used as inputs to the AI. A petrophysical evaluation of volume of clay, porosity and water saturation was also available. 80% of the selected wells (144 out of 181) were used for AI model training, leaving the remaining 20% for model validation. The initial baseline trained model exhibited a RMSE of 0.62. A significant increase of prediction performance (RMSE=0.2) was observed with dataset augmentation: artificial increase of diversity and size of dataset by data windowing, local gradient and second order interaction. It was noticed that all input parameters do not have the same influence on the prediction performance with GR log having the biggest influence. The optimised trained AI model was applied on the validation dataset (37 remaining wells). Outputs were compared to the manual log evaluation and the difference was low and considered acceptable, therefore validating the model. The AI model was deployed on two fields offshore UK in the same geologic setting but with poorer reservoir properties to test its suitability on a regional scale in a blind test. A decreased prediction performance was observed, therefore highlighting the importance of diversifying the composition of the training dataset, in order to make models more robust to regional variations. As a conclusion after data processing and optimisation, the AI model provided meaningful and corroborated outputs. Sensitivity tests to the number of wells in the training set were also carried out and it was found that the AI model only required 25 wells for training, in combination with dataset augmentation, to predict with confidence the reservoir properties. A potential use case is AI prediction in data room contexts for quick evaluations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".