Chemical Stimulation at a Heavy-Oil Field: Key Considerations, Work Flow, and Results
Bibliographic record
Abstract
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 184974, “Chemical-Stimulation Pilot at a Heavy-Oil Field: Key Considerations, Work Flow, and Results,” by Mauricio Gutierrez, Fernando Bonilla, Layonel Gil, and Wilmer Parra, Ecopetrol, and Pablo Campo, SPE, Alex Orozco, and Monica Garcia, Halliburton, prepared for the 2017 SPE Canada Heavy Oil Technical Conference, Calgary, 15–16 February. The paper has not been peer reviewed. Because of current oil and gas industry economics, evaluating the return on investment for any well-intervention campaign is crucial, as is applying an assurance process to help quantify desired production improvement. This paper presents the planning and execution of a matrix-stimulation pilot project in the heavy-oil Chichimene Field in Colombia. The approach is based primarily on a work flow that includes characterizing formation damage, reviewing laboratory tests, validating well selection, and determining economically viable placement and diversion techniques. Damage Mechanisms Heavy-oil reservoirs are prone to almost every formation-damage mechanism known. Damage mechanisms encountered include fines migration, paraffin and asphaltene deposition, various forms of scale, and clay swelling. Many of these damage mechanisms are compounded by the methods used to produce heavy oil, including slotted liners, screens, and gravel packs, which can plug off as a result of any of the damage mechanisms and, over time, further reduce inflow and well performance. A process to identify and characterize formation damage in the Chichimene Field was established. For this purpose, several wells were selected to analyze formation-damage distribution. Reservoir-property data were uploaded into a simulator with a dynamic model to quantify the effects of formation damage attributed to pressure drop in the reservoir. The following damage mechanisms were observed. Drilling- and Workover-Induced Damage. Water, solids, or both, when used in drilling or during workovers, tend to decrease the effective permeability of the formation. Water from drilling fluids contains additives that produce chemical reactions with the formation, which can generate precipitates that plug pore throats. Solids from the drilling and completion fluids also can physically plug or bridge pore throats. Organic Deposition. Organic deposition usually occurs in two forms—paraffin and asphaltenes. Those hydrocarbons classified as paraffins are generally inert. They are resistant to dissolving in acids, bases, and oxidizing agents. Additionally, paraffin deposits often include other materials, such as scale, sand particles, or asphaltenes. Asphaltene deposition is a more subtle form of deposition. It is not usually visible in the field, and extensive laboratory testing is necessary for its detection. Inorganic Deposition. Common forms of inorganic deposition include calcium carbonate scale and gypsum scale. Less common, but more difficult to treat, are iron-rich deposits and silica scale. Numerous methods exist for the removal of inorganic scale, including simple mechanical methods, such as jet washes and complex acid/solvent washes.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".