Years ago, when I asked my mentor what the key to a successful well test was, he said, “Clear objectives, the right equipment, attentive operations, and comprehensive analysis.” I joked that it sounded quite simple and obvious, to which he responded, “Simple and obvious doesn’t mean easy to achieve.”
So, here we go, years later, with my contemplation of the simple and the obvious of a successful well test.
First, establish clear and specific objectives using a systematic approach and align them with all stakeholders. From design and implementation to data collection and analysis, test objectives should remain the go-to framework for decision-making.
The Right Equipment
Advances in equipment enable us to gather data beyond the capabilities of what was previously feasible: high-resolution gauges, wireless telemetry, distributed temperature sensing, real-time flow-control devices, advanced bottomhole and surface sampling techniques, and multiphase flowmeters, to name but a few. We understand that hardware dictates the quality of the data. We should also consider equipment fit for purpose with value of information in mind while remaining committed to safety and tolerant of uncertainty. A deepwater exploration test will require a different set of equipment than a diagnostic fracture injection test (DFIT) or a production-allocation test.
Procedures should be in place to achieve success, such as basis of design, risk assessment, well tests on paper, detailed operation procedures, and meetings (e.g., prejob safety, pretour). But, as Murphy’s Law tells us, what can go wrong will go wrong: Equipment may fail, people may make mistakes. Complacency is the enemy, so operational personnel should always remain alert and keep uncertainty and contingency in mind. As always, safety is the No. 1 objective and the most critical consideration.
Analytical capabilities have progressed since the days of the semilog plot, with, for example, various type curves and near-wellbore/boundary models, carbonate and fracture models, deconvolution, nonlinear modeling, interference tests, horizontal wells, and DFITs. While the new digital era will provide insights from machine learning and automation from massive amounts of information, foundational data still should be collected and quality checked. Subsurface remains inherently a nonunique problem to solve, so, rather than mindlessly fitting the data, the engineer still will need to consider what makes sense with uncertainty in mind.
The papers selected for this issue focus on key factors in achieving a successful well test. They also apply reservoir fundamentals as well as sound engineering judgment, with examples from conventional and unconventional assets.
This Month's Technical Papers
Recommended Additional Reading
SPE 189826 DFIT Analysis in Low-Leakoff Formations: A Duvernay Case Study by Behnam Zanganeh, University of Calgary, et al.
SPE 189840 Reinterpretation of Flow Patterns During DFITs on the Basis of Dynamic Fracture Geometry, Leakoff, and Afterflow by Behnam Zanganeh, University of Calgary, et al.
SPE 189844 Estimating Unpropped-Fracture Conductivity and Compliance From Diagnostic Fracture Injection Tests by Han Yi Wang, The University of Texas at Austin, et al.
|Heejae Lee, SPE, is a senior engineer with ExxonMobil Production Company. He has 18 years of experience in the oil and gas sector, including in simulation research, worldwide exploration/development well testing, and various projects in ventures/development/production as a reservoir engineer. Lee is currently the supervisor for the upstream reservoir engineering integration team, which is home to the well-testing team. He holds a PhD degree in petroleum engineering from The University of Texas at Austin. Lee is a member of the JPT Editorial Committee and can be reached at firstname.lastname@example.org.|
Heejae Lee, SPE, Senior Engineer, ExxonMobil Production Company
01 February 2019
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.
Augmented Artificial Intelligence Improves Data Analytics in Heavy-Oil Reservoirs
The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields.
As you read the examples in this section, you will see that a change is already under way in that the methods that are being used are increasingly not oil-and-gas-specific but instead follow patterns that are being used in other industries.
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