Fracture-driven interactions (FDI’s) occur on most modern drilling and completion programs. These well-to-well interactions can be of benefit, but it is often difficult to quantify the lasting effects of these interactions and benchmark it for comparison. The approach for quantifying the effects of fracture-driven interactions in this paper is to compare the magnitude and duration of communication events identified by chemical tracers for wells in a specific basin. Metrics are established to compare communication between projects to determine the effects of "common contributors" to significant fracture-driven interactions. The common contributors compared in this paper are well spacing, depletion effects from prior production, perforation cluster design, geologic features, and stimulation treatment design. A "Communication Intensity" metric (CI) is established to quantify the amount of communication taking place on a particular pad or project. This is computed for hundreds of projects across a basin or multiple basins. The CI is used as a performance indicator to compare current project-level communication to area or basin-level benchmarks. Case histories are presented in this paper that utilize the CI metric to determine the effects of common contributors of well communication to established basin benchmarks. In addition to project-level communication, localized (well-level) communication events are investigated to determine the cause of the individual event and if any preventative or mitigative strategy could have been applied in the job design or execution phase. Few if any industry benchmarks are available to compare the immediate, intermittent, and lasting effects of fracture-driven interactions (FDI’s) on a project or well level. The results of this paper outline a process for comparing projects to optimize well spacing and fracturing treatment design, as well as reflect on known and unknown features contributing to significant interactions.
The application of data science remains relatively new to the oil and gas industry but continues to gain traction on many projects due to its potential to assist in solving complex problems. The amount and quality of the right type of data can be as much of a limitation as the complex algorithms and programing required. The scope of any data science project should look for easy wins early on and not attempt an all-encompassing solution with the click of a button (although that would be amazing). This paper focuses on several specific applications of data applied to a sizable database to extract useful solutions and provide an approach for data science on future projects. The first step when applying data analytics is to build a suitable database. This might appear rudimentary at first glance, but historical data is seldom catalogued optimally for future projects. This is especially true if specific portions of the recorded data were not known to be of use in solving future problems. The approach to improving the quality of the database for this paper is to establish requirements for the data science objectives and apply this to past, present and future data. Once the data are in the right "format", the extensive process of quality control can begin. Although this part of the paper is not the most exciting, it might be the most important, as most programing yields the same "garbage in = garbage out" equation. After the data have found a home and are quality checked, the data science can be applied. Case studies are presented based on the application of diagnostic data from an extensive project/well database. To leverage historical data in new projects, metrics are created as a benchmarking tool. The case studies in this paper include metrics such as the Known Lateral Contribution (KLC), Heel-to-Toe Ratio (HTR), Communication Intensity (CI), Proppant Efficiency (PE) and stage level performance. These results are compared to additional stimulation and geological information. This paper includes case studies that apply data science to diagnostics on a large scale to deliver actionable results. The results discussed will allow for the utilization of this approach in future projects and provide a roadmap to better understand diagnostic results as they relate to drilling and completion activity.
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