Model‐based multi‐objective particle swarm production optimization for efficient injection/production planning to improve reservoir recovery

Abstract

This study employs an adjusted version of the multi-objective particle swarm optimization (MOPSO) algorithm to plan an optimized reservoir's injection/production strategy. Three case studies, including two water-flooding benchmark models and one gas-condensate problem, are exercised as subjected problems to validate the MOPSO approach. The contradicting values of objectives, long-term net present value (LNPV) versus short-term net present value (SNPV), are obtained so that relying on a Pareto front improves decision-making. In one water-flooded case, inflow control valves of smart wells are considered to be adjusted within the optimization, while in the second case, the optimized well injection rates are control variables. The obtained results for water-flooded reservoirs are shown to optimize the competitive objective functions more than the previous efforts in the literature. Moreover, 10 different permeability maps of the second case are implemented to obtain the optimum injection rates to perform optimization under uncertainty. The MOPSO robustly optimized the production/ injection strategy in the presence of model uncertainty. In the gas-condensate problem, the optimal gas injection rate in the SPE-3 benchmark model is determined. The gas-condensate case's outputs yield a decreased oil saturation result in the reservoir compared to non-optimized production scenarios. Results illustrate that for all cases, MOPSO can provide optimal injection/production scenarios. Therefore, the proposed scheme gives the advantage of deciding between the set of results into a decision-maker to optimize the production program by tradingoff within different strategies. Besides the Pareto fronts with adequate variety and steadiness, a great converging rate is the main advantage of this method.

Scheduling Model for Prefabricated Component Assembly, Production, and Transportation Stage Based on GA for Prefabricated Buildings

“…The discrete numerical constraint for time is shown in equation ( 4). 0,1,...,Sn 2 t = + (4) The non-negative start time constraint for all activities is shown in equation (5). 0, 1, 2,..., 2 Si i n ≥ = + (5) After determining the assembly schedule for the standard layer, the production cycle required for each layer of assembled components can also be determined.…”

“…In solving Multi-Objective Optimization (MOO) problems, common methods include conventional mathematical methods such as weighted sum method, objective programming method, and ε -constraint method. However, these methods also have certain shortcomings, such as relying on the experience of decision-makers, pre-determining the expected values of each Objective Function (OF), and being sensitive to the shape of the search space [4][5]. With the advancement of computer technology, more intelligent optimization algorithms are being used to solve construction scheduling models for prefabricated buildings.…”