Data mining/analysis
This paper introduces in-pipe inspection technologies enabling high-resolution digital measurements of tubular internal diameter and wall thickness for critical downhole applications.
In this study, the authors propose the use of a deep-learning reduced-order surrogate model that can lower computational costs significantly while still maintaining high accuracy for data assimilation or history-matching problems.
This paper describes an approach to creating a digital, interconnected workspace that aligns sensor data with operational context to place the completions engineer back into a central role.
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This paper tests several commercial large language models for information-retrieval tasks for drilling data using zero-shot, in-context learning. -
In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.
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Given the diversity of coiled tubing well-intervention data, many acquisition labels are often missing or inaccurate. The authors of this paper present a multimodal framework that automatically identifies job type and technologies used during an acquisition.
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Almost every day, petroleum engineers are coming to realize that they’ve got an arsenal of good ideas on how to leverage large, messy data sets to add value to their businesses. Those who have enlisted in the Analytics Army have progressed from siloed digitalization attempts to well-concerted digital transformation strategies that reflect high levels of organizational…
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This paper focuses on the vital task of identifying bypassed oil and locating the remaining oil in mature fields, emphasizing the significance of these activities in sustaining efficient oilfield exploitation.
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The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.
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The authors present an open-source framework for the development and evaluation of machine-learning-assisted data-driven models of CO₂ enhanced oil recovery processes to predict oil production and CO₂ retention.
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The authors of this paper propose hybrid models, combining machine learning and a physics-based approach, for rapid production forecasting and reservoir-connectivity characterization using routine injection or production and pressure data.
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his paper investigates the challenges faced in the development of mature and tight fields, primarily resulting from reservoir depletion, high operational costs, and uncertainty in reserves volumetric calculations.
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The industry’s vast untapped data resources have the potential to change how our industry works—if we can piece it together.
