Applications of Machine Learning and Data Mining in SpeedWise® Drilling Analytics: A Case Study
Bibliographic record
Abstract
Abstract The daily drilling report (DDR) contains the daily activities and parameters during drilling and completion (D&C) operations that can be used to identify the bottlenecks and improve efficiency. However, the datasets are large, unstructured, text heavy, not correlated to other datasets, and contain numerous gaps and errors. Thus, conducting any meaningful drilling analytics becomes cumbersome. In this paper, an innovative method is introduced to automatically clean the data and extract intelligent analytics and opportunities from these reports. Natural language processing (NLP) and deep neural network (DNN) models are developed to extract information from unstructured DDRs. Numbers of interest (such as depths, hole sizes, casing sizes, setting depths, etc.) are extracted from text. Drilling phase, non-productive time (NPT) and the associated types are predicted with DNN models. With 30% of the dataset for training, accuracies achieved on the remaining data include 87.5% for drilling phase, 90.7% for time classifications (productive or non-productive), and 89% for associated NPT types. Then, the D&C datasets are integrated with other data sources such as production, geology, reservoir, etc. to generate a set of crucial drilling and reservoir management metrics. The proposed method was successfully applied to several major oil fields (with total of more than 2,000 wells) in the Middle East, North America, and South America. Here, a case study is presented in which the developed method was applied to more than 200 wells drilled from 2012 to 2016 in a major oil field. By using the proposed method, the data processing and aggregation time that used to take months to accomplish was reduced to only a few days. As a result, major types of NPT were rapidly identified, which include rig-related issues such as repair and maintenance (30%), followed by stuck pipe (23%), hole/mud related issues (such as wellbore stability, mud loss, shale swelling, etc.) (20%), and downhole equipment failures and maintenance (14%). Drilling solutions such as contractual advices, improving the mud formulations, and drilling with a rotary steerable system (RSS) were proposed to possibly mitigate the NPT and improve drilling efficiency. Implementation of the proposed solutions eventually resulted in reducing the drilling time and improving capital efficiency. Novel technologies such as NLP, data mining, and machine learning are applied to rapidly QC, mine, integrate and analyze large volumes of D&C data. In addition, this novel approach assists D&C obstacles identification and future plan optimization with evident benefits for improving performance and capital efficiency from a reservoir management perspective.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".