Rheological Hierarchy Optimization Improves Fluid Displacement and Well Integrity, 3 Worldwide Case Studies

Shadravan, Arash; Alegria, Adriana; Castanedo, Rodrigo

doi:10.2118/174773-ms

Day 2 Tue, September 29, 2015 2015

DOI: 10.2118/174773-ms

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Rheological Hierarchy Optimization Improves Fluid Displacement and Well Integrity, 3 Worldwide Case Studies

Arash Shadravan

Adriana Alegria

Rodrigo Castanedo

Abstract: A good cement job is the Achilles' heel of sustainable production from oil and gas wells. Improving zonal isolation and the integrity of wells worldwide is the priority of well integrity engineers. Proper cement designs and job execution assure sustainable production with ideal zonal isolation. The US unconventional horizontal well drilling has been increasing over the recent years. There are several solutions and best practices including maintaining the rheological and friction pressure hierarchy of the fluid… Show more

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Cited by 8 publications

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Machine Learning Leads Cost Effective Intelligent Fluid Design: Fluid Engineering Perspective

Shadravan

Tarrahi

2016

Day 1 Wed, April 20, 2016

Self Cite

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What is the catch for intelligent fluid design? Several hours of laboratory testing (some successful, some unsuccessful), dealing with various complex chemistries, listening to multiple people, as long as they are around, some of whom educated with the science called Chemistry rationalizing and trying to match the theories with the laboratory results? Getting superstitious about the physics of the problem due to lack of data? Dealing with contradictory data, and convincing ourselves, engineers, that this is the way to go? Seeing laboratory personnel taking pride on the number of the tests that have conducted while we still need to conduct more testing to determine the next additive concentration for the new well with different downhole condition? Facing with enormous non-productive time and dealing with the fear of failing to deliver to the job and lose the tender, on the service side, or choose the most cost effective scenario for your well, on the operator side? Or shall we go with what Joe proposed and reduce the risk? He has been there for years. How much more testing is enough for a known system, area or formation?Chemists are creating better and novel formulations, and laboratory personnel are doing their job following the API, ideally around the world, but fluid engineers are still testing or manage testing the fluids by trial and error whether in fracturing, cementing or drilling fluids. While designing fluids by trial and error in each fluid research group might sound unrelated to the other, all have one in common because of the traditional approach in fluid design. Trial and error testing lacks the vision for sustainable fluid knowledge management and intelligent design.The purpose of this paper is to challenge the status quo in fluid design, discuss what is missing in stepping towards intelligent fluid design and identify the challenges and limitations to execute the proposed method. Disadvantages of the traditional fluid designs, lack of knowledge/data management or sharing and the cost incurred are elaborated. Machine learning algorithms lead as the prime solution for intelligent fluid design, saving thousands of hours of laboratory testing, efficiently integrating sizable experimental database and optimizing the operational cost and delivery.

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Machine Learning Leads Cost Effective Intelligent Fluid Design: Fluid Engineering Perspective

Shadravan

Tarrahi

2016

Day 1 Wed, April 20, 2016

Self Cite

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Inverse Modeling for Fluid System Characterization Through Machine Learning Algorithms

Tarrahi

Shadravan

2016

Day 1 Wed, April 20, 2016

Self Cite

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Inverse fluid modeling facilities designing drilling, spacer, cement or fracturing fluids. This approach uses several test data available at the laboratory databases for fluid characterization. Without machine learning algorithms it is very hard for fluid engineers to rationalize a plethora of fluid system information, transform them into knowledge and make intelligent and cost-effective decisions to design a fluid system with desired properties. Therefore, trial and error method is considered to be very costly and misleading. Today, there is a need for an expert system which uses all the available fluid design data stored in a database by which we can benefit from its insights for smart fluid designs. This predictive tool suggests a composition for drilling fluids, spacer fluids, fracturing fluids and cement slurries by implementing a machine learning algorithm on imported experimental data.This study investigates the inverse modeling fluid characterization through machine learning algorithms. A laboratory dataset of measured fluid properties and their corresponding fluid system characteristics are employed as training data to the machine learning routine. GPR as a machine learning method considerably reduced the costs of testing, optimized the material use, integrated available experimental data and eliminated the user bias. This practical nonlinear regression method fosters an efficient and fast prediction analysis which do not require including complex physics of the underlying intricate chemical fluid behavior while integrating all available data from different databases.

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First Implementation of a Resilient and Self-Sealing Cementing System to Optimise Long-Term Zonal Isolation in Challenging Offshore Wells - Review of 3 Case Histories in Malaysia

Brandl

Alegria

Ismail

et al. 2016

Day 1 Mon, August 22, 2016

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Three consecutively drilled, highly deviated, high-temperature offshore wells in Malaysia with multiple thin payzones and planned close perforation distances required the use of a self-sealing cementing technology with optimized mechanical properties to minimize the risk of unwanted fluid communication that would necessitate immediate abandonment. This paper reviews the successful implementation of the corresponding cementing system starting from its development, through the jobs' planning phases, to the operational executions and evaluation with results. A pre-job study was executed to design a suitable cementing system, including thorough lab tests, CFD simulations for optimized fluid displacement efficiencies, and cement sheath stress modelling to optimize the cement mechanical properties. A novel HPHT multi-function test cell (MFTC) was used to measure in-situ ability of fractured cement specimens to seal hydrocarbon flows under the given simulated downhole conditions. Yard tests were conducted to ensure quality and field applicability of the engineered cementing system. With the help of the simulations and lab tests, the mud-spacer-cement fluid trains could be optimized for fluid friction and density hierarchies during each 7-in. liner cementing job for effective synthetic oil-based mud removal and cement slurry placement covering the desired >1,000 m annular length in highly deviated wells with low frac gradients at temperatures up to 300 °F. Combining the self-sealing additive with a mineral fiber in the tested cement designs significantly reduced the Young's modulus of the set cement while the tensile strength and the Poisson's ratio improved, resulting in a higher resilience of the cement sheath to survive the anticipated downhole stresses from the completion program. The crack-and-seal tests demonstrated the cement system could repeatedly seal cracks or micro-annuli up to at least 0.15 mm (0.006-in.) due to the swelling of the self-sealing additive in the presence of a hydrocarbon flow at differential pressures below 1,000 psi and temperatures up to at least 300 °F. The expansion of the set cement was also optimized to enhance the bond to formation and pipe. The evaluation of the pre-job planning, job execution as per program, post-job simulated pressure matches, positive ultrasonic cement logging results, and the fact that no issues have been observed during or after the job indicated well-isolated intervals. The novel cementing concept combines several advanced design features (self-sealing in the presence of hydrocarbons, expansion, resilient mechanical properties) that improve long-term zonal isolation in critical well scenarios.

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Rheological Hierarchy Optimization Improves Fluid Displacement and Well Integrity, 3 Worldwide Case Studies

Cited by 8 publications

References 40 publications

Machine Learning Leads Cost Effective Intelligent Fluid Design: Fluid Engineering Perspective

Machine Learning Leads Cost Effective Intelligent Fluid Design: Fluid Engineering Perspective

Inverse Modeling for Fluid System Characterization Through Machine Learning Algorithms

First Implementation of a Resilient and Self-Sealing Cementing System to Optimise Long-Term Zonal Isolation in Challenging Offshore Wells - Review of 3 Case Histories in Malaysia

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