Waterflood Application in Semi-Consolidated Heavy Oil Reservoirs Developed by Horizontal Well Technology
Bibliographic record
Abstract
Waterflooding is the most common injection process in oil industry and none of the more complex enhanced oil recovery (EOR) methods enjoys the widespread applicability of waterflooding today. Operators in the field have extensive experience in application of waterflooding to conventional light oil reservoirs. However, the waterflood mechanisms and its optimum operating practices in heavy oil reservoirs are poorly understood. Since the primary recovery factor in heavy oil reservoirs is between 5-10% of the original oil in place (OOIP), there is a need for enhanced techniques to increase recovery from these reservoirs. In Western Canada, waterflooding has been applied to lower viscosity heavy oil reservoirs called medium oil reservoirs and somewhat surprisingly, it has increased the ultimate recovery factor of these pools by 100%. Recently, operators have extended the waterflood applications to heavier oil reservoirs which have been developed by horizontal well technology. These reservoirs are usually semi-consolidated sandstones with oil viscosities of more than 1000 cP, which historically, did not produce oil economically with Cold Heavy Oil Production with Sand (CHOPS) technology. This thesis has investigated several field applications of waterflood process in these heavy oil pools developed by horizontal well technology to better understand the recovery mechanism of waterflood in this type of reservoirs and determine important parameters, which affect the performance of the process. Learnings from empirical data, analogous pools and numerical simulation studies are used to develop an initially low water saturated and semi-consolidated heavy oil pool with horizontal wells and implement waterflood application successfully. The PVT data, core flooding test results and detailed geology data are incorporated in a numerical simulation model. Heterogeneous history matched numerical simulation model is used to conduct a sensitivity analysis for several inter-well spacing and then the predicted production forecasts are used in economic models to determine the optimum development spacing. The reservoir was successfully developed with drilling horizontal wells during 2012 and 2013. Then the simulation model was updated based on production profiles from newly drilled horizontal wells. The field conformance plots from analogous pool that has been waterflooded for more than two decades are used to calibrate the reservoir simulation model to predict the waterflood recovery factor and conduct a sensitivity analysis to different operating practices. The waterflood production profile for Voidage Replacement Ratios (VRR) of less than one is generated and economic models are used to estimate the heavy oil waterflood reserves and evaluate the viability of the project. Finally, the generated production profile from the model is compared to actual waterflood results to validate the model. The result of this work shows that the waterflood recovery mechanism in heavy oil reservoirs is different from conventional displacement process and the recovery of the process can be improved significantly by applying optimized operating conditions in the field. Predicting the heavy oil waterflood profiles remains as a main challenge for industry and there is a need to develop a tool to forecast the waterflood response in heavy oil reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".