An Efficient Workflow for Production Allocation During Water Flooding

Abstract

This paper presents an efficient production optimization scheme for an oil reservoir undergoing water injection by optimizing the production rate for each well. In this approach, an adaptive version of simulated annealing (ASA) is used in two steps. The optimization variables updating in the first stage is associated with a coarse grid model. In the second step, the fine grid model is used to provide more details in final solution search. The proposed method is formulated as a constrained optimization problem defining a desired objective function and a set of existing field/facility constraints. The use of polytope in the ASA ensures the best solution in each iteration. The objective function is based on net present value (NPV). The initial oil production rates for each well come from capacity and property of each well. The coarse grid block model is generated based on average horizon permeability. The proposed optimization workflow was implemented for a field sector model. The results showed that the improved rates optimize the total oil production. The optimization of oil production rates and total water injection rate leads to increase in the total oil production from 315.616 MSm3 (our initial guess) to 440.184 MSm3, and the recovery factor is increased to 26.37%; however, the initial rates are much higher than the optimized rates. Beside this, the recovery factor of optimized production schedule with optimized total injection rate is 3.26% larger than the initial production schedule with optimized total water injection rate.

Optimal rate allocation for production and injection wells in an oil and gas field for enhanced profitability

“…Moreover, an adaptive simulated annealing algorithm was employed by Azamipour et al, for the production optimization of a field undergoing water injection: therein, optimization time reduction was attained by sequentially implementing coarse and fine grids without discernible loss in model accuracy. 33 The authors improved their optimization approach in a subsequent article, using a hybrid genetic algorithm. 34 As part of their improvements, streamline simulation has also been adopted for the determination of good initial guesses for the water injection rates.…”

“…A complex economic objective function with business considerations has been adopted therein toward enhancing planning decisions over a field's lifetime. Moreover, an adaptive simulated annealing algorithm was employed by Azamipour et al, for the production optimization of a field undergoing water injection: therein, optimization time reduction was attained by sequentially implementing coarse and fine grids without discernible loss in model accuracy . The authors improved their optimization approach in a subsequent article, using a hybrid genetic algorithm .…”

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

“…Unlike single processing services with limited capabilities, geospatial service chains implement multiple functions to meet users' needs. Users frequently composite multiple services into a service chain based on the relationship between services, commonly employed in the fields of hydrology [1][2][3][4] and the environment [5,6], resulting in value-added service [7].…”

Microscopic Determination of Remaining Oil Distribution in Sandstones With Different Permeability Scales Using Computed Tomography Scanning

“…What's more, researchers also used NMR methods to quantitatively study the residual oil distribution and pore size distribution using different NMR T2 spectrums [7][8][9][10]. Research has shown that porous systems containing remaining oil are significantly influenced by the pore scale entrapment characteristics of the oil phase [11,12].…”