Analysis of Athabasca Oil Sands Investigates SAGD Performance Variability
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 195348, “Steam-Assisted Gravity Drainage Performance Variability—Analysis of Actual Production Data for 28 Athabasca Oil Sands Well Pairs,” by Quang T. Doan, Vincano; S.M. Farouq Ali, SPE, University of Houston; and Thomas B. Tan, SPE, Petrostudies Consultants, prepared for the 2019 SPE Western Regional Meeting, San Jose, California, 23–26 April. The paper has not been peer reviewed. Steam-assisted gravity drainage (SAGD) performance in bitumen-recovery projects in Alberta is affected by geological deposits, reservoir quality, and operational experience. The authors reviewed and analyzed actual field production and injection data for 28 Athabasca oil-sands-deposit SAGD well pairs (WPs). Based on analysis of field-production data, a numerical model was built and calibrated against production data from two of the poorer-performing WPs among the 28 studied. Agreement between simulated and actual cumulative oil and steam/oil ratio (SOR) was within 10% after 6 years of operations on a first-iteration basis. Problem and Investigation Methodology A survey of the literature reveals that some aspects of the SAGD process, particularly with regard to the behavior of gas, still largely are not understood. While successful history-matching simulations of SAGD WPs exist for several different projects, these rely on dead-oil pressure/volume/temperature treatment and exclude any findings and discussion on gas production. Such history-matched models likely would need to be modified significantly to be useful for modeling the wind-down stage of SAGD operations. Recent numerical studies modeling the generation of aquathermolysis gases focused on the injection of noncondensable gas into a mature SAGD steam chamber. In preparation for a series of numerical studies on aspects of SAGD performance, a reservoir model simple enough to be relatively adaptable for different geological settings was built. Such a model had to be calibrated rigorously with actual production data to provide a high degree of confidence in its results and predictive capability in appropriate contexts. The investigation in the complete paper is aimed at addressing several fundamental aspects of SAGD operations, including the effects of reservoir heterogeneities and the behavior of gas. The first part of the complete paper is focused on reviewing and analyzing actual field production data spanning more than 1,700 days from 28 SAGD WPs from four different pads (A, B, C, and D) of the Jackfish 1 project; this synopsis will not include that extensive data. Jackfish 1 Project. Jackfish 1 consists of 42 WPs divided into six pads (including the 28 WPs in Pads A, B, C, and D analyzed for the authors’ study) and is part of the geological oil-sands trend of the Athabasca oil-sands deposit. Jackfish 1 has a nameplate capacity of 35,000 BOPD, with a designed SOR of 2.7. Consistently exceeding 90% of its nameplate capacity since first steam, it is commonly considered to be a successful SAGD project. The results of data analysis for the WPs studied indicated the following: Considerable variance exists in the recovery performance of SAGD WPs on the same pad. Considering the small drainage area of the 7-WP pad (800×800 m), such variances bear important implications for the planning and execution of SAGD projects (particularly greenfield development but also with regard to brownfield expansion). The authors stress that forecasting project productivity should not be based on a single-WP, deterministic simulation model without any consideration for distributions in reservoir geology and parameters affecting SAGD performance. This assessment is equally applicable in the estimation of reserves.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".