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This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 189823, “Machine Learning Applied To Optimize Duvernay Well Performance,” by Braden Bowie, SPE, Apache, prepared for the 2018 SPE Canada Unconventional Resources Conference, Calgary, 13–14 March. The paper has not been peer reviewed. This paper discusses how machine learning by use of multiple linear regression and a neural network was used to optimize completions and well designs in the Duvernay shale. The methodology revealed solutions that could save more than $1 million per well and potentially deliver an improvement in well performance of greater than 50%. The work flow described rigorously analyzes the relationships between a significant number of well-completion variables, predicts results, and performs optimizations for ideal outcomes. The work flow is not Duvernay-specific and can be applied to other basins and formations. Introduction A fundamental problem for machine learning in many industries is that a responding variable is controlled not by one but by a number of predictor variables. Inferring the relationship between the responding variable and the predictor variables is of key importance. Interactions between predictor variables and noise in the data complicate matters further. This problem can be solved with multiple linear regression or a neural network, both of which use all of the predictor variables together. However, care must be taken to obtain a model that is truly predictive and not merely a result of overfitting the data. In unconventional oil and gas reservoirs, well performance generally is characterized either at the well level by detailed technical work such as rate-transient analysis, microseismic, and other techniques or at the field level by statistical methods with ranges for production performance. Refinement of this statistical interpretation generally involves normalizing for only one or two key parameters, such as lateral length or tonnage. Additionally, wells usually are grouped or excluded entirely from the population for various reasons, such as substandard completion design. This introduces bias in the selected wells and reduces the sample size. As a result, this approach is limited to the key variables identified and the bias introduced by the well population selected. The idea of using a neural network has been executed successfully in the past to optimize completions. However, data sets were limited. Recently, the use of machine learning has grown substantially by integrating more variables in the analysis, which reduces reservoir uncertainty. The goal of the work flow described in the complete paper is to improve on previous methodology by rigorously and statistically refining estimates for well performance without excluding wells and to recommend which variables are and are not influencing well performance. The goal was accomplished through machine learning in the form of a multiple linear regression and a neural network, comparing the results from both.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.62)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play > Shale Gas Play (0.61)
- Geology > Geological Subdiscipline (0.48)
- North America > Canada > Saskatchewan > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- North America > Canada > Northwest Territories > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- North America > Canada > Manitoba > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- (2 more...)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Well performance, inflow performance (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Neural networks (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Subsea processing is an evolving technology in response to ultradeepwater hydrocarbon development and has the potential to unlock a significant amount of hydrocarbon resources. Therefore, subsea processing systems with industrial standards are becoming more favorable in the development of offshore hydrocarbon plays and are replacing traditional systems. The objective of this paper is to examine the features of subsea fluid-processing technologies and capabilities, and compare the advantages and disadvantages of different facility types. The advantage of subsea processing systems is that they allow fluids to be boosted from longer tieback distances. Constraints include operation efficiency, produced-water- and sand-handling capabilities, and the system's ability to handle hydrates/scale.
Just as there are shortcomings of deterministic models that can be avoided with probabilistic models, the latter have their associated pitfalls as well. Adding uncertainty, by replacing single estimate inputs with probability distributions, requires the user to exercise caution on several fronts. Without going into exhaustive detail we offer a couple of illustrations. First, the probabilistic model is more complicated. It demands more documentation and more attention to logical structure.
- Reservoir Description and Dynamics > Reservoir Simulation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Management > Risk Management and Decision-Making > Decision-making processes (0.50)
- Facilities Design, Construction and Operation > Offshore Facilities and Subsea Systems > Floating production systems (0.48)
Abstract Health impact assessments (HIAs) have been used for a number of years in the public sector to assess the potential for impacts associated with urban redevelopment projects. HIAs can also serve as a valuable tool in the evaluation of capital projects in the oil and gas sector. Conducting HIAs for industry capital projects, however, is not common and few people or organizations have experience performing HIAs in this context. While some guidance about HIAs is available, the focus is more on what HIAs are all about rather than how to perform one. These circumstances led us to develop HIA practices that are compatible with the generally accepted practices reported by others, but are nonetheless customized to meet our business requirements and expectations regarding HIAs. This paper discusses the elements of our HIA practices that we believe are key to producing effective HIAs. These include involving the "right people" at the right time, and HIA training to create a pool of health experts to support the process.
- North America > United States (0.28)
- South America > Brazil (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Health & Medicine > Therapeutic Area > Infections and Infectious Diseases (0.93)
- Information Technology > Security & Privacy (0.46)
- Information Technology > Data Science (0.46)
This article is a synopsis of paper SPE 68818, "A Rule-Induction Algorithm for Application to Petrophysical, Seismic, Geological, and Reservoir Data," by Clayton V. Deutsch and YuLong Xie, U. of Alberta, and A. Stan Cullick, Landmark Graphics, originally presented at the 2001 SPE Western Regional Meeting, Bakersfield, California, 26-30 March.