Shale Gas Drilling Performance Break Through in Wei Yuan– Relentless Scientific and Engineering Approaches for the Unconventional Resources in Central China
Bibliographic record
Abstract
Abstract Being the world's third largest shale gas producer after the US and Canada, China delivered an output of 9 billion cubic meters (bcm) in 2017. China has the world's largest technically recoverable reserves of shale gas, of which US Energy Information Administration (EIA) estimates at 31.6 tcm, 68% higher than shale reserves in the US. Unlike the US who started to explore shale gas in the 1980s, China only completed the first shale gas well in 2011. Development of shale gas resources is expected to play a vital role in China's enthusiastically planned transition to a low-carbon energy future. On September 14th, 2016, Chinese National Energy Board released Shale Gas Development Plan 2016-2020. In the plan, shale gas production goal was set at 30 bcm for 2020. With an average shale gas production of 20MCM per well per year, it is estimated that a minimum of 1500 horizontal wells with 1000m lateral length are needed by the year of 2020. The question arises whether what kind of drilling performance is needed to meet the aggressive development target. In less than a decade, Petro China, its subsidiaries and contractors have made significant breakthroughs in shale gas exploration, not only in capacity, but also drilling techniques. The paper captures the success and lessons that the drillers had gained in the last 7 years in terms of drilling performance. It is well known that China shale gas reserves are in geologically challenging areas. The challenges consisted of hard formations with kicks, losses, frequent stuck pipe and over pressure formation. The problems were amplified by high geological formation dip, faults, and stratigraphic uncertainties. In this harsh drilling environment, rate of penetration was slow, trajectory control is difficult, mud weight and circulating pressure are high, downhole torsional vibration, drilling torque and stick&slip are high, rig equipment and downhole tools fail prematurely, and non-productive time is excessive. Over the years, the team had demonstrated that with systematic, scientific and engineering drilling approaches, a considerable improvement in drilling performance can be achieved. To deliver and execute the optimized drilling approaches, high intregration and synergy between each drilling segment are required. These approaches are nothing new in the drilling world, these are optimization in Well Plan, Mud Properties, Rig Capacity & Drilling Parameters, Bottome Hole Aseembly (BHA) selection and design, best Drilling Practice and Drilling Operation Efficiency. These are all part of a formula to success; the key is to rightly balance each one of them. The team sucessfully reduce average well days from 120 to 30 in one particular field. Along the way, the team also identify a few more components to the formula of success, with that, the short-term goal shall be further reducing the well days to 25 days, and less than 20 days in long term.
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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".