Bibliographic record
Abstract
Abstract This paper presents the use of machine learning via a multiple linear regression and a neural network to solve the complex problem of optimizing completions and well designs in the Duvernay shale. Solutions were revealed that could save over a million dollars per well, along with the potential for more than 50% improvement in well performance. This was accomplished through a workflow that rigorously analyzed the relationships between a multitude of well completion variables, generated predictions of future results, and performed optimizations for ideal outcomes. Most importantly, this workflow is not Duvernay specific, and can easily be applied to other basins and formations. This is a fundamental problem in many industries, in that a responding variable is controlled not just by one predictor variable, but by a number of predictor variables. Inferring the relationship between the responding variable and the predictor variables is then of key importance. Interactions between predictor variables, as well as noise in the data, complicate matters further. This problem can be solved with a multiple linear regression or a neural network, both of which utilizes all predictor variables together. However, care must be taken to obtain a model that is truly predictive and not a result of overfitting the data. The workflow was applied to 262 Duvernay wells, ranging from dry gas to volatile oil. No wells were excluded for operational or geological reasons, a strength of this methodology. By not excluding any wells, the model could maximize learnings and establish statistical reasons for the variances in well performance observed. The final model achieved very high predictive power, correctly predicting 78% of the variance in well performance on 52 wells the model hadn't been trained on. Conclusions were quite significant, including: Indicating virtually no benefit from more expensive fracturing procedures, such as using ceramic or resin coasted proppant, or having hybrid fluid systems, offering savings of over a million dollars per well in the Duvernay.No benefit from placing wells on an azimuth (parallel to the minimum horizontal stress) vs. a North-South orientation (~45° off azimuth). This allows potentially large savings on a land ownership system not aligned to this direction, by allowing simpler pad design in achieving the same aerial coverage of reservoir depletion.Confirming total fracture tonnage as a key driver of well performance.Suggesting fracture pump rate is associated with better well performance and should be investigated further. These conclusions would have been very difficult to derive without expensive strategic testing on numerous wells with rigorous control of the completions and geological inputs. When compared to recent well performance of six operators, the neural network predicted substantial ability to improve well performance by varying parameters under operator control. Potential improvement ranged from 19% to 97%, showing large potential improvement for all operators.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".