Modeling Usage of Degradable Fluid-Loss Additive Revamps Fracturing Design

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 210633, “Multimaterial Multiphysics Modeling Coupled With Post-Fracturing Production-Flow Simulations: Revamping Hydraulic Fracturing Design Strategy,” by Abdul Muqtadir Khan, SPE, Vadim Isaev, and Ludmila Belyakova, SPE, Schlumberger. The paper has not been peer reviewed. _ With the aid of a multiphysics simulator, the authors recently presented a novel use of a degradable fluid-loss additive (DFLA) as a fracture geometry additive to reduce the pad volume while achieving the same geometry. In this complete paper, the authors extend advanced slurry flow modeling with production simulation to propose the optimal design strategy for fracturing, which may challenge current treatment-design conventions. A novel work flow was developed, with four coupled working blocks of laboratory, slurry flow modeling, production analysis, and machine learning. The new digital framework proposes solutions for the limitations of current methodology. Introduction Across different generations, multiple fluid additives have been introduced to target specific treatment objectives; one of these that has been available for years, but that has been underused, is fluid-loss additive (FLA). Theoretical and field bases have been presented for its use in the following applications: - Conventional prolific reservoirs to mitigate high fluid loss and create sufficient fracture geometry - Tight gas formations with high tectonic influence to mitigate poroelastic effects and fissure-dependent enhanced leakoff - Unconventional reservoirs to avoid repression during production - Openhole wells to control multiple fracture initiation - Multipad wells to avoid interwell communications and fracture hits - Fracturing treatments aimed at optimizing and reducing crosslinked pad volume Despite these benefits, the primary issue of using an FLA is its potential to damage fracture conductivity and the reservoir. Some developments of nondamaging FLA have been presented but are either operationally complex or difficult to use. This subject requires special attention to investigate damage and production loss through laboratory studies, numerical models, and machine-learning models and define a tailored strategy with DFLA. Digital Framework for Treatment Design The authors present an end-to-end work flow from chemistry development to an automated FLA-assisted fracturing design tailored for a specific reservoir and well. The framework is broadly split into three blocks: laboratory testing, fracture modeling, and production modeling. These blocks can be sensitized together with multiple case runs and a large digital database. This data set then can be subjected to regression and classification algorithms. With enough data built in the toolbox, a fully coupled data- and physics-based forward model can be created that can output the fracture design with DFLA type, concentration, and pad volume with inputs of reservoir properties and pore-throat descriptions. The tool infrastructure is built to allow the model to retrain and update with real field data addition. In the first block, multiple laboratory tests evaluate DFLA performance. The laboratory tests include static and dynamic leakoff tests to evaluate reduction in spurt loss and wall-cake building leakoff coefficient with varying concentrations of DFLA in the fracturing fluid. Additionally, core-flow tests are performed to understand the effect of DFLA on regained permeability and validate the postulation of its nondamaging nature.