Abstract
The ongoing low oil price environment has profound impact on all the operators and service providers. Efficiency improvements and cost reductions are two key strategies to capture the opportunities in current situation. It is now imperative for engineers to improve engineering practices to gain efficiency and drive down the cost. However, in well stimulation practices, it is vital to all stakeholders to obtain fracturing fluids with desirable fluid viscosity, and thermal and chemical properties with cost as low as reasonably possible.
Preparing fracturing fluids with high-hardness produced water is one option to drive down cost. Such a practice eliminates the need to transport fresh water and dispose wastewater. However, it may not be possible because of the reaction tendencies between cations and functioning polymers within fracturing fluids. Although combinations of chemicals including polymers, chemical additives, stabilizers, and buffers can be added to prevent such reactions, the dosages of each component are often hard to quantify and often dependent on the experience of field engineers. A fast prognosis tool has been developed to facilitate such a decision-making process by identifying the lowest possible dosages of chemicals dependent on produced water hardness. With such a prognosis tool, it is now possible to make more educated and cost-effective plans to optimize the engineering practice.
In this work, the underlying physics of hard water damage to fracturing fluids is discussed, followed by a demonstration of the simulations that reproduced the lab rheology experiment results. The viscosity profiles of a number of rheology experiments were first matched as model calibrations. The effects of different stabilizers were then discussed by parametric comparison of different cases. With the predictive model successfully capturing the reaction system, a dynamic algorithm was built to compute the lowest cost possible solution of functioning fracturing fluids in a given system. Due to the non-linear nature of the chemical reaction system, a searching algorithm was implemented to minimize the cost of fracturing fluids as functions of local produced water hardness, cost of each chemical component and operational expenses.
This work demonstrates that good engineering practices are achieved through carefully understanding and modeling a working system, and through dynamic planning of the overall workflow with cost optimization as a target. All in the form of a prognosis tool that could be easily implemented in the current fracturing fluid preparation work flow.
