Forecasting the Effects of Reservoir Souring From Waterflooding a Formation Containing Siderite
Bibliographic record
Abstract
Abstract A computer model has been developed to investigate the potential effects of water injection in an offshore oilfield on H2S biogeneration and its subsequent production from the reservoir. The model is based on mechanistic algorithms employed in previous reservoir souring models developed for the Prudhoe Bay and Kuparuk River Fields (Alaska) and the Ekofisk Field (Norway). These mechanisms include the biogeneration of H2S, the partitioning of H2S between oil and water in the waterflooded portions of the reservoir, the reaction of dissolved H2S with siderite to form immobile iron sulfides, and the transport of H2S, oil, water, and gas to producing wells. The reservoir is currently being seawater flooded although produced water reinjection (PWRI) will soon be initiated. In mid-2006, after less than three years of waterflood at relatively low injection rates, H2S was detected in the gas of several producing wells located close to injectors. It is known that PWRI will result in increased H2S production. The model was initially calibrated via history match to determine algorithm coefficients associated with siderite scavenging and microbial nutrient availability within the reservoir during seawater flooding. This calibration required historical produced water sulfate concentrations and H2S mass production rates in order to ascertain actual biogenic sulfate reduction levels and the remaining amount of H2S scavenged by the siderite following its partitioning to reservoir fluids. Results indicate that while scavenging by the siderite is likely occurring at a relatively high level, the H2S production rate will increase significantly once water injection rates are increased and PWRI begins. This paper presents insight as to the importance of water geochemistry in the reservoir souring process and the potential for siderite reactivity, distribution and availability to affect H2S production.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".