A Data Driven Approach to Predict Frictional Pressure Losses in Polymer-Based Fluids
- Sercan Gul (The University of Texas at Austin) | Mitchell David Johnson (The University of Texas at Austin) | Ali Karimi Vajargah (The University of Texas at Austin) | Zheren Ma (The University of Texas at Austin) | Besmir Buranaj Hoxha (The University of Texas at Austin) | Eric van Oort (The University of Texas at Austin)
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- Society of Petroleum Engineers
- SPE/IADC International Drilling Conference and Exhibition, 5-7 March, The Hague, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2019. SPE/IADC Drilling Conference and Exhibition
- 7.6 Information Management and Systems, 1.11.2 Drilling Fluid Selection and Formulation (Chemistry, Properties), 7.6.4 Data Mining, 1.11 Drilling Fluids and Materials, 1.7.5 Well Control, 7.6.6 Artificial Intelligence, 1.6 Drilling Operations, 3 Production and Well Operations, 7 Management and Information, 3 Production and Well Operations, 1.7 Pressure Management
- polymer based fluids, managed pressure drilling, machine learning, data analytics, turbulent flow
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Managing drilling margins in challenging wells requires precise prediction of frictional pressure losses and equivalent circulating density (ECD). Current hydraulic models in the industry fail to accurately predict the frictional pressure losses of certain mud formulations in turbulent flow due to the complex behavior of long-chain polymer additives. These additives facilitate friction reduction in certain flow regimes. This reduction depends on several parameters, such as molecular weight and chemical composition of the polymers, making it difficult to quantify using existing models. In this paper, a data-driven approach is proposed to precisely predict frictional pressure losses for polymer-based fluids.
A flow-loop was constructed to measure frictional pressure-losses of several polymer-based non-Newtonian fluids under laminar, transitional, and turbulent flow regimes. Pressure loss data was obtained for fluids with different polymer concentrations at various temperatures using differential pressure measurement. A database of the experimental data was compiled and used to build a predictive model for frictional pressure prediction using advanced machine learning techniques. The proposed approach has general validity and can be extended to any type of well construction fluid (used in drilling, completion, stimulation, or workover).
Results for frictional pressure loss predictions from the proposed data-driven approach were compared with both the experimental data and widely used industry models. An excellent agreement was observed between the proposed approach and the experimental results, demonstrating the applicability of this approach for hydraulic modeling of polymer-based fluids. The improvements are particularly noticeable at higher polymer concentrations in the turbulent flow regime, where the average percentage discrepancy between the existing models and the experimental data can be as high as 45%.
The proposed approach in this study is particularly valuable for wells with a narrow drilling margin and concerns about the ability to manage ECDs (such as slim hole wells, deepwater wells or extended reach wells). It can assist with better planning and avoiding non-productive time and drilling problems such as lost circulation, stuck pipe, wellbore instability, and well control events. Its adaptability to a wide range of fluids using an expanded database makes it particularly attractive as a practical solution to this challenging problem.
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