Well testing and surveillance have always been, and continue to be, the foundations of reservoir management. Fundamental data, such as pressure, rate, and temperature, and fluid samples are collected during a well test and used to investigate the subsurface. With advancements in modern technology such as smart wells, distributed pressure/temperature, real-time measurements, and extended-reach drilling, we are facing conditions with increasing complexity and unprecedented amounts of data.
In 2017, several key partnership announcements with major information-technology companies were noticed. The oil and gas industry is going through a digital transformation reinforced by data science; terms such as data cloud/lake, machine learning, Internet of Things, high-performance computing, automation, and model management are the new buzzwords. When analytics are applied correctly, they will provide valuable insights, especially in cases such as unconventional reservoirs with significant numbers of wells. In order to make the transformation a success, many of the industry’s leading experts agree that quality as well as quantity of data should be important.
Experience teaches us that the subsurface is always more complex than we expect, feedback is not instantaneous, and issues are difficult to mitigate in the reservoir scale. Hence, essential and information-rich data such as exploration/production-well tests, proper fluid samples, and sufficient/periodic surveillance should still be used effectively as indicators to peel off layers of uncertainty in the complex subsurface. Thus, we conclude that reservoir-engineering fundamentals must still be applied and data should still be quality checked, especially when collecting and applying analytics from a massive amount of information. The age-old “garbage in, garbage out” mantra will continue to apply in the upcoming era of data science.
The papers selected for this issue cover advances and opportunities in well testing. They also apply reservoir fundamentals as well as sound engineering judgment, using quantity but also quality data sets from conventional and unconventional assets.
This Month's Technical Papers
Recommended Additional Reading
IPTC 18924 Current State and Future Trends of Wireline-Formation-Testing Downhole Fluid Analysis for Improved Reservoir-Fluid Evaluation by S.R. Ramaswami, Shell International Exploration and Production, et al.
SPE 187348 New Variable Compliance Method for Estimating In-Situ Stress and Leakoff From DFIT Data by HanYi Wang, The University of Texas at Austin, et al.
SPE 185795 Step-Rate Test as a Way To Understand Well Performance in Fractured Carbonates by A. Shchipanov, IRIS, et al.
Heejae Lee, SPE, Senior Engineer, ExxonMobil Production Company
01 February 2018
A Neural-Network Approach for Modeling a Water-Distribution System
The authors present a new data-driven approach to estimate the injection rate in all noninstrumented wells in a large waterflooding operation accurately.
Intelligent Completion in Laterals Becomes a Reality
A producer and a service company have collaborated to develop a downhole system to merge multilateral technology and intelligent completions to create “smart laterals.”
Intelligent Fields Technology
What I find impressive is the number of people who were hiding in normal discipline jobs who are coming out of the closet with their Python scripts. And, it’s working. In many ways, order is coming to the mess, efficiency is coming to tiresome manual activities, and richness is coming to decisions.
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