From Coke to Crude: ExxonMobil Pumping Refinery-Sourced Proppant in Hundreds of Permian Basin Wells
Bibliographic record
Abstract
ExxonMobil recently shared new details about an in‑house innovation being scaled across the Permian Basin where the company produces more than 1.6 million BOE/D as the region’s largest operator. Over the past couple of years, the US supermajor has steadily increased its use of petroleum coke, or petcoke, sourced from its own refineries in Canada and Texas to stimulate shale wells and the results show why. ExxonMobil reports that horizontal wells pumped with regular sand plus a dose of its petcoke, which looks like black sand to the naked eye, consistently deliver 7 to 18% more production in their first year than those hydraulically fractured with sand only. The competitive advantage petcoke holds as a proppant boils down to its ability to move with sand, then reach parts of the fracture network that sand cannot. And by demonstrating this, ExxonMobil has effectively repurposed one of its lowest‑margin hydrocarbon products into a subsurface technology that generates far greater value when used at the very start of an oil well’s life. ExxonMobil presented details about some of the first trials of petcoke proppant at the Unconventional Resources Technology Conference (URTeC) in Houston earlier this year. In URTeC 4264319, the company highlights that the proprietary version of petcoke it is using is significantly lighter and more buoyant than sand, yet offers comparable strength and remains equally inert to fracturing fluids and the formation. “These properties help transport the proppant further into fractures and keep more fracture area open compared to sand. This allows us to lower overall drilling and completion costs and increase production rates—so we’re bullish on it,” said Jimmy Jin, an ExxonMobil research engineer focused on advanced completions and well management. Bullish seems an apt description of the company’s outlook on adoption. Jin told JPT that ExxonMobil is moving “at an accelerated pace” to deploy petcoke proppant across its Permian footprint. After successful trials last year on about 50 wells, he said ExxonMobil now aims to complete more than 200 wells with petcoke by year‑end, and at full scale expects to use up to 2 million tons annually. For now, the oil company is keeping quiet on some of the specifics, including its ratio of petcoke to sand. ExxonMobil’s authors said at URTeC that the trials used wells with spacing decisions made a year earlier and that now the operator is weighing how the new proppant should influence its well spacing and stacking strategies going forward. The bad news for other producers hoping to follow suit is that ExxonMobil holds two patents for the innovation and wants to protect its intellectual property going forward. That means it has no plans to license the idea or sell the material to others for use as a proppant. The company maintains that it is always evaluating its options and will do whatever makes the most business sense.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".