Formation evaluation
The authors describe the effectiveness of an electromagnetic look-ahead service while drilling in terms of providing accurate formation profiles ahead of the bit to optimize geostopping efficiency.
In the past year, publications on CO2, natural gas, and hydrogen storage have increasingly focused on the design, evaluation, and optimization of storage plans. These efforts encompass a broad spectrum of challenges and innovations, including the expansion of storage reservoirs from depleted gas fields and saline aquifers to stratified carbonate formations and heavy-o…
This study integrates recently developed formation testing with advanced petrophysical logging to address limitations of traditional methods in volcanic breccia formations.
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The complete paper describes a tool developed to evaluate and characterize thin beds or laminations in a mature offshore field. -
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The utility of mud gas data so far has been limited to fluid typing, formation evaluation, and interwell geological and petrophysical correlation. The ongoing digital transformation has presented the opportunity to increase the utility of, and get more value from, the abundant and rich mud gas data. This article raises the question of whether getting more from mud gas…
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This paper describes a technology with the capability to map and detect lithology changes ahead of the bit in real time.
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Traditionally, wireline or logging-while-drilling formation-testing technology has been the most accepted means of reservoir fluid characterization. But when those fluids are contaminated by mud filtrate, acoustic data can be used for fluid characterization during downhole sampling to successfully track and quantify small changes in oil compressibility.
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This paper describes a technique used to derive stresses for a gas-storage field and then calibrate the 1D mechanical earth model of the field.
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The US Department of Energy’s Office of Fossil Energy and Carbon Management awarded a total of $600,000 to four software developers who won a contest to support FECM’s Science-Informed Machine Learning to Accelerate Real-Time Decisions in the Subsurface Initiative.
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The authors outline a case study involving the integration of static measurements to improve understanding of reservoir properties and support more-accurate reservoir evaluation.
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The authors develop a representative geostatistically based 3D model that preserves geological elements and eliminates uncertainty of reservoir properties and volumetric estimates for a Libyan field.
