Automatic Optimization of Oilfield-Scale-Inhibitor Squeeze Treatments Delivered by Diving-Support Vessel

Abstract

Summary Oilfield-scale deposition is one of the important flow-assurance challenges facing the oil industry. There are a number of methods to mitigate oilfield scale, such as reducing sulfates in the injected brine, reducing water flow, removing damage by using dissolvers or physically by milling or reperforating, and inhibition, which is particularly recommended if a severe risk of sulfate-scale deposition is present. Inhibition consists of injecting a chemical that prevents the deposition of scale, either by stopping nucleation or by retarding crystal growth. The inhibiting chemicals are either injected in a dedicated continuous line or bullheaded as a batch treatment into the formation, commonly known as a scale-squeeze treatment. In general, scale-squeeze treatments consist of the following stages: preflush to condition the formation or act as a buffer to displace tubing fluids; the main treatment, where the main pill of chemical is injected; overflush to displace the chemical deep into the reservoir; a shut-in stage to allow further chemical retention; and placing the well back in production. The well will be protected as long as the concentration of the chemical in the produced brine is greater than a certain threshold, commonly known as minimum inhibitor concentration (MIC). This value is usually between 1 and 20 ppm. The most important factor in a squeeze-treatment design is the squeeze lifetime, which is determined by the volume of water or days of production where the chemical-return concentration is greater than the MIC. The main purpose of this paper is to describe the automatic optimization of squeeze-treatment designs using an optimization algorithm, in particular particle-swarm optimization (PSO). The algorithm provides a number of optimal designs, which result in squeeze lifetimes close to the target. To determine the most efficient design of the optimal designs identified by the algorithm, the following objectives were considered: operational-deployment costs, chemical cost, total-injected-water volume, and squeeze-treatment lifetime. Operational-deployment costs include the support vessel, pump, and tank hire. There might not be a single design optimizing all objectives, and thus the problem becomes a multiobjective optimization. Therefore, a number of Pareto optimal solutions exist. These designs are not dominated by any other design and cannot be bettered. Calculating the Pareto is essential to identify the most efficient design (i.e., the most cost-effective design.)

Review of Synthesis and Evaluation of Inhibitor Nanomaterials for Oilfield Mineral Scale Control

“…Coupled with the elevated salinity, scaling threats can take place in subsurface, production tubing, subsea flowlines, and topsides facilities (Fan et al, 2012a). It has been reported that scale deposition is responsible for ∼20% of downtime for a major oil company across all regions (Vazquez et al, 2007). In shale play, water evaporation can be enormous due to hydraulic fracturing and pressure drawdown, resulting in a severe scaling threat (Vankeuren et al, 2017;Chang et al, 2019).…”

“…Although the aforementioned Ca-DTPMP SINMs can significantly improve inhibitor performance in terms of transportability in formation medium and squeeze lifetime, these materials might not be suitable for application in the field characterized by low water cut and/or high water sensitivity (Collins and Hewartson, 2002;Guan et al, 2006;Vazquez et al, 2007Vazquez et al, , 2016Chen et al, 2010). Since the aforementioned nanomaterials are water based, the water in the SINM package can damage the formation and impair well productivity due to mechanisms such as clay mobilization and pore constriction.…”

Optimization of Fracturing Parameters by Modified Genetic Algorithm in Shale Gas Reservoir

“…The intelligent optimization method is optimizing fracturing parameters based on intelligent optimization algorithms, such as Genetic Algorithm (GA) [29] and Particle Swarm Optimization (PSO) [30]. Compared with the traditional optimization method, the intelligent optimization method can achieve the overall optimization of fracturing parameters and has better optimization accuracy.…”