Data-Driven Well Pad Development Performance Review: Focus on the Role of Liner Design on Well Performance
Bibliographic record
Abstract
Abstract Steam Assisted Gravity Drainage (SAGD) is the dominant in-situ method for oil production in Western Canada. The current study analyzed the relative performance of various well-completion practices using data from 4,000 well pairs that were drilled over a decade. The data analysis provided a unique opportunity to find best operating practices. The scope of this paper is to review the performance of major thermal projects in Canada and investigating the effect of liner design and Flow Control Devices (FCDs) on well pair performance and development. Cumulative oil production and cumulative steam oil ratio (cSOR) were used as the key metrics in comparing the well performance in a SAGD operation. However, to compare different pads and different projects, it was critical to normalize the data with geological variation, well length, well spacing, and with consideration to the well failure rate, remedial completion and re-drills. In this paper we review seven thermal projects of four key operators with almost 3,500 wells and 1,200 well pairs in operation as early as 1996. All geoscience, and production/injection data have been extracted from public databases and utilized to develop a data-driven model. The reservoir thickness variation for each well was determined using available geoscience data, and through the development of a geological model based on the available core data and well logs. The model was used to define the drainage volume for each well pair, which in turn was used to assign a geological ranking to the well. The cumulative oil production and cSOR were then normalized with the geological ranking and the size of the net drainage volume. The number of well pairs in each pad and the cumulative pad production were normalized against the number of days in production and their relative decline, which allowed for comparison between pads within the same project, as well as pads from other projects. The cumulative production of the active pads in each project was used to compare the relative performance of different projects. Also, we separated the projects and wells based on their use of FCDs in the producer and injector to compare the relative performance of each technology in the field. This paper is the initial phase of the study on the role of completion design on relative well and well pad performance. The results will help completion and production engineers to better understand the well pair and pad relative performance and how to normalize the oil production data against geological variation to compare performance.
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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 | 0.001 | 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) | 0.000 | 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".