well spacing
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The authors of this paper describe a solution using machine-learning techniques to predict sandstone distribution and, to some extent, automate the process of optimizing well placement. -
The authors of this paper define a work flow that constrains solutions that match models and field observations and obtains a more-representative model for forecasting and optimizing fracture behavior.
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This paper presents a numerical simulation work flow, with emphasis on hydraulic fracture simulation, that optimizes well spacing and completion design simultaneously.
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The authors write that child-well performance increases with spacing and decreases with infill timing and that the parent cumulative production at child-well completion is an effective indicator of child-well performance.
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This paper describes a novel distributed quasi-Newton derivative-free optimization method for reservoir-performance-optimization problems.
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The complete paper builds on existing tools in the literature to quantify the effect of changing well spacing on well productivity for a given completion design, using a new, simple, intuitive empirical equation.
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In this paper, the authors describe a model that uses augmented artificial intelligence to optimize well spacing by use of data sculpting, domain and feature engineering, and machine learning.
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A well-pattern-design work flow proved able to identify substantially better patterns than the traditional approach for a giant mature field.
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MPD was used to successfully drill through a pore pressure ramp and address a well-control event in conjunction with conventional methods.
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Present industry solutions to the challenge of well spacing involve expensive geomechanical Earth modeling or fracture-geometry monitoring that is time-consuming, data-intensive, and geography-specific.
