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Technology and partnerships play a pivotal role in how the oil industry finds and produces energy from frontier regions and brownfields, both now and in the future.
Data quality and mission clarity matter more than ever, according to experts speaking at this year’s International Petroleum Technology Conference in Kuala Lumpur.
Guest Editorial: 2025 Perspective: Digital Transformation in Oil and Gas—From Evolution to Shockwave
The path forward is not just about automation—it is about augmentation. AI is not replacing human expertise; it is amplifying it. Those who master this balance will define the future of oil and gas in a rapidly evolving energy landscape.
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In the Marcellus, Repsol is slicing and dicing legacy data to evolve its completions strategy, while in the Permian, ExxonMobil is mastering the 4-mile lateral drillout using lessons learned. -
The service giant shares new details about its automated fracturing spreads that slash human operator workload by 88%.
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This month’s column reviews the evolution of our membership, focusing on young professional members whose demography is a point of concern.
B3 Insight and Nanometrics plan to integrate data from seismic monitoring with a water and subsurface data analytics platform.
The new burner, created with the help of machine learning and additive manufacturing, promises high methane destruction efficiency and combustion stability even in windy conditions.
The trial phase of the agentic program used AI agents and combined large-language-model technology with data collected from more than 15% of ADNOC’s onshore and offshore wells.
SLB said it plans to integrate INT’s technology into its digital data and artificial intelligence platforms.
Located 230 km south of Abu Dhabi, the onshore Shah field produces around 70,000 B/D of crude.
A new report from GlobalData provides an overview of the digitalization efforts within the industry and their potential to transform operations.
The acquisition, expected to close in early 2025, will bring Optime Subsea’s umbilical-less technology into Halliburton’s Testing and Subsea division.
Moving from use cases to enterprisewide AI is more than a technology challenge. It requires anchoring on value, feedback, and innovation.
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The authors propose a hybrid virtual flow and pressure metering algorithm that merges physics-based and machine-learning models for enhanced data collection. -
The authors introduce a novel framework combining dynamic mode decomposition, a data-driven model-reduction technique, with direct data assimilation to streamline the calibration of carbon-dioxide plume evolution models. -
A detailed comparison of two leading software platforms demonstrates the effective application of these platforms in modeling complex reservoir dynamics and biochemical reactions in geological formations for risk assessment in underground hydrogen storage. -
Regional pore-pressure variations in the Leonardian- and Wolfcampian-age producing strata in the Midland and Delaware basins are studied using a variety of subsurface data.
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This paper discusses the concept, applications, and continual evolution of a new 3D temperature and spectral-acoustics modeling and logging approach. -
This paper presents various functionalities and benefits of a monitoring tool developed for and used with all critical flowmeters in the operator’s production system. -
In this paper, an energy-based 3D fracture-reconstruction method is proposed to derive the complex fracture network from microseismic data in a shale gas reservoir. -
The authors write that, by wireline formation testing of a sandstone formation adjacent to a sand/shale laminated reservoir in the Weizhou shale-oil region of the Beibu Gulf, key reservoir information can be directly obtained.
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This paper presents a novel workflow for using electromagnetic resistivity-based reservoir mapping logging-while-drilling technologies for successful well placement and multilayer mapping in low-resistivity, low-contrast, thinly laminated clastic reservoirs.
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This paper proposes a methodology for preventing drillstring fatigue and failure in deep wells with large shallow doglegs.
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In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.
