Simulation of Bacterial Souring Control in an Alberta Heavy-Oil Reservoir
Bibliographic record
Abstract
Abstract This paper presents the development of a simulation model describing the bacterial-induced souring of an Alberta heavy-oil producing field and its remediation via injection of nitrate. An area of the field with extensive bacterial activity was selected for the simulation study. The primary production and water-flood injection phases were history matched with basic reservoir maps and injection/production operating conditions adjusted via an automatic history match algorithm. Extensive chemical and microbiological compositional data for injected and produced waters were collected and analyzed at the University of Calgary and a mathematical model of the souring process and its remediation treatment was developed based on the information collected. Simulation indicated the volumetric distribution of the hydrogen sulfide over time and how the injected nitrate was distributed and acted to achieve souring control. Predicted individual production well responses were shown to be consistent with field observations, and issues regarding improved monitoring and design of laboratory experiments for future field operations are highlighted. Hence, simulation can be a useful tool in understanding and designing remedial treatments to bacterial souring in the Western Canada Sedimentary Basin. Introduction The field of interest is a shallow glauconitic sandstone in the Medicine Hat area of the Western Canada Sedimentary Basin which is marginal marine to fluvial incised valley infill. The lower channel has good intergranular porosity and high permeability while the upper channel has a mixed composition that can lead to porosity and permeability variations and flow barriers. The area has been under primary production since the early 1980's with the use of horizontal producers by the mid- 1990's. Water injection started in early 2000's to recapture drive energy but souring began to appear after several years of produced water re-injection in this previously sweet field. As the reservoir is close to a high population area, remediation action was deemed necessary. The operator contacted Baker Hughes Incorporated to design a treatment scheme and the University of Calgary to monitor the process and to conduct laboratory experiments. Treatment with nitrate began in May 2007 and consisted of continuous injection of 2.4 mM nitrate over a period of more than one year. Thereafter it was decided to augment this treatment with high concentration "squeeze" treatments which also had a beneficial effect(1). Reservoir simulation was also deemed useful to quantify the reservoir flow paths and resultant bacterial activities. This paper describes the implementation and insight provided by the simulation study to the understanding of the process effectiveness. Reservoir Characteristics and Study Area General characteristics of this reservoir are given in Table 1. Discussions with the field operator lead to the decision to focus the simulation modelling in an area of particular interest, centered around an injector/producer pattern showing interesting response characteristics of souring and the remediation treatments. Figure 1 shows the pattern area of interest which consists of two vertical injectors internal to the pattern plus five boundary injectors. Internal to this region are six horizontal producers.
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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) | 0.000 | 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".