Identification and Prediction of Wells Susceptible to Sulfide Stress Cracking
Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 160489, ’Identification of SSC (Sulfide-Stress-Cracking) -Susceptible Wells and Risk Prediction,’ by Tapan Chakrabarty and Richard J. Smith, Imperial Oil Resources, prepared for the 2012 SPE Heavy Oil Conference Canada, Calgary, 12-14 June. The paper has not been peer reviewed. Hydrogen sulfide (H2S) generated by aquathermolysis—a high-temperature reaction of condensed steam (water) with sulfur-bearing bitumen in the reservoir rock—may increase the risk of sulfide stress cracking (SSC) in cyclic-steam-stimulation (CSS) wells. Identifying the SSC-susceptible wells is important in terms of reducing SSC risk by allocating resources to and implementing proactive intervention measures at the SSC-susceptible wells. A comprehensive research program, with a dedicated instrumented CSS well as the centerpiece, has been undertaken with the objectives of characterizing H2S evolution in the wellbore and developing a tool for identifying the SSC-susceptible wells. Introduction Imperial Oil is using CSS commercially to recover bitumen from the Clearwater formation at the Cold Lake field in Alberta, Canada. In this process, the reservoir is stimulated by injecting high-pressure/high-temperature steam to reduce the bitumen viscosity and produce the thinned bitumen through the same well in a cyclical manner. In a given well, steam injection is through the casing, liquid (bitumen and condensed steam) production is through the tubing, and gas production is mainly through the casing annulus. It is the casing that is susceptible to SSC, as shown in a small casing section retrieved from a Cold Lake CSS well (Fig. 1). SSC in a CSS well is induced by H2S that is generated by the reaction, termed aquathermolysis, between condensed steam and sulfur-bearing bitumen. SSC increases with an increase in H2S partial pressure (pH2S) and with a decrease in well temperature. The inverse temperature effect is attributed to the diffusion of hydrogen atoms—a product of the reaction between H2S and iron—through the casing metal matrix. At a lower temperature, the hydro-gen diffusion is slower and the hydrogen accumulation inside the casing wall is higher, leading to a higher stress build-up and an increased SSC susceptibility. To reduce the risk of SSC in CSS wells at Cold Lake, Imperial Oil has implemented an extensive casing-integrity-assurance program that includes Measuring the H2S during production in all the wells as they cool down to lower than 70°C Shutting in an SSC-susceptible well Purging the casing annulus of a shut-in well with nitrogen to lower the H2S level in the wellbore
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".