Big Data Yields Completion Optimization: Using Drilling Data to Optimize Completion Efficiency in a Low Permeability Formation

Lehman, Lyle; Jackson, Kale; Noblett, Bruce R.

doi:10.2118/181273-ms

Cited by 7 publications

References 10 publications

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Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

Alzahabi

Trindade

Kamel

et al. 2020

J Petrol Explor Prod Technol

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Machine learning techniques have fundamentally altered how oil and gas industry practitioners design fracture operations. In this paper, we perform data analytics utilizing response surface methodology (RSM), a group of statistical techniques that develop a functional relationship between an output variable of interest and several associated input variables, to optimize the output. We apply RSM to optimize horizontal well production based on initial production (IP) of horizontal oil wells for 180 days (IP180 Oil), as a function of five input variables: reservoir type, fracturing fluid (gal/ft), proppants (lbm/ft), cluster spacing, and stage length (ft). The RSM model correlates the initial production of each well to the input variables via a single equation, thus allowing for exploration of the fitted response surface in order to maximize production. Although the choice of the five inputs is made based primarily after consultation with industry professionals, we validate our selection by also applying an assortment of data-analytics-based methods that attempt to rank variable importance and thereby identify completion variables that may be predictive of initial production. The findings rank all five variables above the 50th percentile, thus indicating that the chosen variables have merit. This procedure is applied to a dataset of 201 horizontal wells from the Wolfcamp formations. The model fits reasonably well, with R2 = 61%, a very significant F-statistic p value, and a predicted versus observed values scatterplot indicating a good fit. The RSM analysis suggests that, within the feasible space defined by this dataset, maximum values of IP 180 Oil may be obtained by setting the fracturing fluid in gal/ft at approximately 1972, while simultaneously maximizing the remaining input variables (proppant loading, cluster spacing, stage length). The outcome indicates the possible directions to be taken in seeking a global optimum initial production for the setting of completion variables. Iteration of this scheme may lead to a near-optimum global solution. The real utility of this work may be indicating the way different studies may be designed to optimize production, each with its own selection of inputs, and ultimately be combined in a meta-analysis.

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Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

Alzahabi

Trindade

Kamel

et al. 2020

J Petrol Explor Prod Technol

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Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods

2021

J. Phys.: Conf. Ser.

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In unconventional oil and gas resources, especially shale oil and gas resources with extremely low permeability and porosity, in order to develop effectively, it is necessary to establish perforation and multi-stage fracturing. The machine learning algorithm k-means clustering was used to cluster the five features selected to describe reservoir properties: shale content, porosity, total organic carbon content and those reflecting rock mechanical properties: Young’s modulus and Poisson’s ratio. The data were classified in a high-dimensional space to determine different perforation fracture stages. The classification algorithm XGboost was then used to predict different perforation stages using four conventional logging curves GR, NPRL, VP and DEN. The K-means clustering algorithm based on Euclidean distance can well classify the selected features in the high-dimensional space. The Hopkins statistics of clustering trend is 0.94, showing a good clustering trend. When the classification algorithm is used for prediction, the average accuracy is 0.92, the average recall rate is 0.90, and the average F1 score is 0.90, which can predict different perforated fracture stages well and optimize the design of the perforated fracture stage.

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Big Data Analytics Drive EOR Projects

Xiao

Sun

2017

Day 1 Tue, September 05, 2017

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This paper presents the analytics of physics-driven big data in reservoir hydrodynamic simulation and parameter optimization for EOR projects in Daqing oilfield. An application model proposed in this study enables reservoir engineers to dynamically adjust parameters for numerical reservoir simulation. Huge amount of data are collected through Internet of Things (IOT) technology by installing large amounts of small scale and cost effective sensor devices in various systems of EOR projects, including injection system, injectors, production system, down hole pumps, surface gathering system, etc. Realtime data from various sensor sources are then integrated and normalized into the big data system. The big data system integrates and establishes the relationships between difference data source components, e.g., down hole pump, choke, separator, power plant, compressor, water treatment, export infrastructure within scope of EOR projects. Then the big data application model determines dynamic parameters used for the inputs of numerical reservoir simulation. Big data system integrates different data sources and is used to calculate production index, PVT parameters, pressure data, properties of oil and gas on a daily-sequence. Those parameters were then treated as the inputs of existing numerical reservoir simulation models. Hydrodynamics of EOR projects were predicted and the operation parameters on a field scale were adjusted based on the simulation results, such as well pattern and space, optimization of water injection parameters, choke size, chemical slug size, etc. Compared with the previous numerical reservoir simulation, the prediction error was reduced by more than 46% with the help of big data application model. Big data application model integrates different data source into an application model. It helps predict the reservoir dynamics with much more accuracy in the aspects of numerical reservoir simulation.

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Big Data Yields Completion Optimization: Using Drilling Data to Optimize Completion Efficiency in a Low Permeability Formation

Cited by 7 publications

References 10 publications

Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods

Big Data Analytics Drive EOR Projects

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