Bibliographic record
Abstract
This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 189823, “Machine Learning Applied To Optimize Duvernay Well Performance,” by Braden Bowie, SPE, Apache, prepared for the 2018 SPE Canada Unconventional Resources Conference, Calgary, 13–14 March. The paper has not been peer reviewed. This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale. The methodology revealed solutions that could save more than $1 million per well and potentially deliver an improvement in well performance of greater than 50%. The work flow described rigorously analyzes the relationships between a significant number of well-completion variables, predicts results, and performs optimizations for ideal outcomes. The work flow is not Duvernay-specific and can be applied to other basins and formations. Introduction A fundamental problem for machine learning in many industries is that a responding variable is controlled not by one but by a number of predictor variables. Inferring the relationship between the responding variable and the predictor variables is of key importance. Interactions between predictor variables and noise in the data complicate matters further. This problem can be solved with multiple linear regression or a neural network, both of which use all of the predictor variables together. However, care must be taken to obtain a model that is truly predictive and not merely a result of overfitting the data. In unconventional oil and gas reservoirs, well performance generally is characterized either at the well level by detailed technical work such as rate-transient analysis, microseismic, and other techniques or at the field level by statistical methods with ranges for production performance. Refinement of this statistical interpretation generally involves normalizing for only one or two key parameters, such as lateral length or tonnage. Additionally, wells usually are grouped or excluded entirely from the population for various reasons, such as substandard completion design. This introduces bias in the selected wells and reduces the sample size. As a result, this approach is limited to the key variables identified and the bias introduced by the well population selected. The idea of using a neural network has been executed successfully in the past to optimize completions. However, data sets were limited. Recently, the use of machine learning has grown substantially by integrating more variables in the analysis, which reduces reservoir uncertainty. The goal of the work flow described in the complete paper is to improve on previous methodology by rigorously and statistically refining estimates for well performance without excluding wells and to recommend which variables are and are not influencing well performance. The goal was accomplished through machine learning in the form of a multiple linear regression and a neural network, comparing the results from both.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".