Water, Water Everywhere: Using ML and Game Theory To Win at Produced-Water Forecasting
Advanced machine-learning methods combined with aspects of game theory are helping operators understand the drivers of water production and improve forecasting and economics in unconventional basins.
OTC 2020 Tech Papers Offer a Look Into the Future of Offshore
The Offshore Technology Conference was cancelled for the first time ever due to the COVID-19 pandemic. But the flow of ideas continues. As proof, this curated summary of technical papers highlights unique concepts that might someday reduce the offshore sector’s heavy cost burdens.
Machine-Learning Approach Improves Deepwater Facility Uptime
This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights.
Fatigue Prediction for Extended Riser Life and Improved Vessel-Response Analysis
This paper presents a fatigue-prediction methodology designed to extend the life of unbonded flexible risers and improve the accuracy of floating production, storage, and offloading vessel response analysis.
Prescriptive Analytics Aids Completion Optimization in Unconventionals
This paper discusses a prescriptive analytics framework to optimize completions in the Permian Basin.
Upstream Digitalization Is Proving Itself in the Real World
For the upstream industry, where improvement in efficiency or production can drive significant financial results, there is no question that the size of the digital prize is huge. So are the challenges.
Well-Completion System Supported by Machine Learning Maximizes Asset Value
In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection.
Dynamometer-Card Classification Uses Machine Learning
The complete paper explains the steps taken to improve surveillance of beam pumps using dynamometer-card data and machine-learning techniques and reviews lessons learned from executing the operator’s first artificial intelligence project.
Artificial-Intelligence-Driven Timelines Help Optimize Well Life Cycle
This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events.
Artificial Intelligence Transforms Offshore Analog Fields Into Digital Fields
This paper details how artificial intelligence was used to capture analog field-gauge data with a dramatic reduction of cost and an increase in reliability.
Optimization Work Flow Maximizes Value of Unconventional Fields
Well spacing optimization is one of the more important considerations in unconventional field development.
Data-Driven Management Strategy Can Reduce Environmental Effect of Production Plants
This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant.
How Southeast Asian Upstream Operators Are Digitalizing
Malaysia’s Petronas, Shell Malaysia, and Thailand’s PTTEP are now in the midst of full-scale digital adoption. The companies are beginning to see results, but none is counting on a “big bang” in development of the technology soon.
Analytics Solution Helps Identify Rod-Pump Failure at the Wellhead
This paper presents an analytics solution for identifying rod-pump failure capable of automated dynacard recognition at the wellhead that uses an ensemble of ML models.
Machine Learning Optimizes Duvernay Shale-Well Performance
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.
Pattern-Based History Matching Maintains Consistency for Complex-Facies Reservoirs
A challenging problem of automated history-matching work flows is ensuring that, after applying updates to previous models, the resulting history-matched models remain consistent geologically.
Machine-Learning Approach Identifies Wolfcamp Reservoirs
This paper discusses a project with the objective of leveraging prestack and poststack seismic data in order to reconstruct 3D images of thin, discontinuous, oil-filled packstone pay facies of the Upper and Lower Wolfcamp formation.
Chesapeake Teams With Analytics Firm To Improve Asset Performance
The Oklahoma City independent has a new-look portfolio and new operational and financial priorities. And now it has enlisted an energy research firm to leverage advanced analytics and machine learning to help get the most out of its assets.
Predictive-Maintenance Approach Uses Machine Learning To Increase Efficiency
This paper focuses on compressor systems associated with major production deferments. An advanced machine-learning approach is presented for determining anomalous behavior to predict a potential trip and probable root cause with sufficient warning to allow for intervention.
Reframing Exploration Strategy Optimizes Project Value
The objective of this case study is to describe a specific approach to establishing an exploration strategy at the initial stage on the basis of not only uncertainty reduction, but also early business-case development and maximization of future economic value.
Can Machine Learning Mitigate Frac Hits?
BHGE is developing an analytics and machine-learning approach that offers descriptive and predictive insights on frac hits, with the aim of eventually offering a real-time monitoring capability to be deployed during frac jobs.
Liquids-Rich Permian Showcased as a Model Basin
The SPE Liquids-Rich Basin Conference in Midland, Texas, merged topics that have dominated recent industry discussions: technologies centered on big data, interwell communication, and Permian Basin production.
Halliburton Aims For Better Wells With Automated Fracturing
The pressure pumping giant turns to algorithms to get better fractures and fewer problems.
Finding Meaning, Application for the Much-Discussed "Digital Twin"
An increasingly buzzy term tossed around at industry events, “digital twin” is leveraging data analytics, machine learning, and artificial intelligence to improve efficiencies from design to decommissioning.
Artificial Intelligence and Human Expertise Form Powerful Combination for Performance Improvement
Anadarko’s CEO and a company board member who heads a venture-backed software developer for artificial intelligence (AI) discuss the key role that AI will play in the oil and gas industry’s future.
Halliburton-Microsoft Strategic Alliance Hopes To Speed Industry Transformation
Halliburton and Microsoft believe that by working together they can step up the pace of digital transformation across the E&P industry.
Artificial Intelligence Holds Promise for Seismic and Drilling Data
As artificial intelligence makes a significant impact on various industries, an expert examines the roles it could play in streamlining oil and gas operations in the near future.
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