Completion Design Optimization as a Function of Reservoir Quality in Tight Reservoirs: A Big Data Approach
Bibliographic record
Abstract
Summary Over the past decade, more than 40,000 multifractured horizontal wells (MFHWs) were completed in the Western Canada Sedimentary Basin (WCSB), making Canada the third largest oil exporter in 2022. However, this process consumed more than 56 billion gallons of water, with more than 30 billion gallons unrecovered—equivalent to the annual water consumption of a city of 1 million for nearly 5 years. This raises concerns about water use efficiency in fracturing operations. Can big data mining reveal the relationship between reservoir quality, fracturing design parameters, and process efficiency? This research aims to develop a hybrid analytical and machine learning (ML) workflow to optimize completion design as a function of reservoir quality in tight reservoirs. To achieve this objective, we analyze data from more than 14,000 wells in the Montney formation. Using core analysis and well log data, we develop a support vector machine (SVM) to predict permeability and a fuzzy clustering model to estimate fracturability. We categorize Montney’s lithology through hierarchical clustering. With 3D kriging interpolation, we fill missing data and create high-resolution 3D petrophysical maps, which help derive a reservoir quality index (RQI) map to identify the sweet spots for completing new wells. We introduce a stage placement efficiency (SPE) metric to evaluate fracture stage placements in-line with the RQI. Finally, we develop neural network–based proxies that predict well performance based on reservoir quality, geomechanical properties, and completion design, incorporating more than 40 completion parameters, the RQI, and historical production data. The results reveal a higher degree of homogeneity in the upper Montney compared to the middle and lower segments. Generally, completion design parameters significantly impact MFHW productivity more than formation characteristics. Applying the proposed proxy to Montney-oil MFHWs could increase fracturing water recovery by 94.4%, reduce injected water volume by 22%, and boost cumulative oil production by 37.9%. The proxy aims to enhance fracturing water efficiency. A 1% reduction in freshwater consumption in the WCSB could save more than 100 million gallons of fresh water and cut CO2 emissions by up to 2,000 tons, equivalent to removing more than 1,000 gasoline cars from Canadian roads.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".