Augmented AI Framework for Well Performance Prediction and Opportunity Identification in Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Many important business decisions and planning in unconventional reservoirs rely on a reliable forecast on well performance. Common practices like statistical type curves, analytical methods, and numerical simulation are not well suited to incorporate all the complexities involving rock/fluid properties, geological parameters, artificial lift systems, well and completion designs, etc. In this work, we introduce a novel "Augmented AI" (Artificial Intelligence) workflow for reliable forecasting of unconventional well performance and show its impact on decision making. Augmented AI represents smart integration of artificial intelligence and domain knowledge. In the application of well performance forecast, a smart DCA algorithm automatically estimates the short- and long-term performance of the historical wells; a spectrum of well attributes are aggregated/transformed with the consideration of uncertainty and robustness for training and prediction. Boosting and bootstrap tree-based models are ensembled to maximize the model generalization capability. In contrast to the commonly seen black-box modeling practices, the factor-specific impacts are deconvoluted, allowing for validation of the underlying physics. Furthermore, this gives guidelines for future well planning and completion designs. A case study is presented, where the workflow is implemented. Multi-disciplinary data (logs, completions, maps, fluid properties, etc.) from thousands of wells were integrated. During the feature engineering step, raw data was converted to a set of meaningful parameters leveraging the domain knowledge. As an example, some of the features were combined, some were transformed, and others were normalized. Then a machine learning model was created using an ensemble approach. The models showed a good model accuracy on the training, testing, and validation dataset. Leveraging the predictive model, thousands of field development opportunities including new vertical wells, new horizontal wells, recompletions, and completion optimization were identified that resulted in increased production, increased reserves, and improved capital efficiency. Using the model explanation techniques, the impact of various parameters on the well performance was quantified that resulted in best practices for future drilling and completion design.
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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) | 0.001 | 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) | 0.002 | 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".