... Abstract Population-based (gradient-free) methods have become attractive for solving optimization problems since they have been known to locate "optimal" (best) positions within a search space. In this study, we investigate the use of three different gradient-free optimization methods namely...

... Abstract This study presents an efficient gradient-based production optimization method that uses a deep-learning-based proxy model for the prediction of state variables (such as pressures and saturations) and well outputs (such as bottomhole pressures and injection rates) to solve nonlinearly...

... Abstract This paper introduces a novel approach to optimize the allocation of injection and production wells in geothermal systems, based on the Look-Ahead Models (LAMs) framework that was originally designed for hydrocarbon reservoir optimization using numerical simulation. The goal...

... pressure increased to compensate for increasing friction losses. From these boundary conditions, it follows that for given reservoir parameters, recooling conditions, mass flows, and borehole geometries, there is only one optimal back pressure for the turbine. This, in turn, significantly determines...

... and stability of the output. production chemistry voltage hydrogen efficiency h2s management corrosion inhibition exchanger consumption concentration correlation integration electrolyzer electrolysis optimization oxygen corrosion management oilfield chemistry operation reaction hydrogen...

... Summary Secure and efficient geological CO 2 storage decisions can be supported by models of subsurface porous structure and of the fluid flow in the porous network. Geological and flow modeling is employed in order to optimize the CO 2 storage process during the lifetime of the operation...

... optimization china lstm generative adversarial network-based framework algorithm geological subdiscipline pearson correlation coefficient accurate dts logging curve generation prediction Introduction The share wave slowness (DTS) logging curve is crucial for oil and gas development, serving...

... Abstract This study utilizes Computational Fluid Dynamics (CFD), implemented through ANSYS-Fluent, to address the environmental impact of oil and gas extraction by optimizing methane gas extraction efficiencies in Cased-Hole Gravel Pack (CHGP) completions. By focusing on the perforation...

... correspond spe reservoir simulation conference cquest grid injector grid europe government enhanced recovery producer 1 scenario ai grid design history matching fast reservoir simulation data-driven model modifier optimization Reservoir simulation with grids connecting wells (flow network...

... Traditional workflows iterating on data assimilation and optimization under uncertainty have been further developed, framing this process as a close loop reservoir monitoring ( Jansen et al. 2009 , Shirangi et al. 2017). The latest ingredient necessary for a traditional robust optimization...

...-SQP) variants, to handle the nonlinearly constrained production optimization problems. The LS-SQP workflow specifically, has been successfully utilized by Nguyen et al. (2022) , in conjunction with the lexicographic method, to solve a bi-objective optimization problem in CCUS on Brugge reservoir...

... on dynamic changes in injection and offtake. Thus, fracture propagation was monitored carefully through periodic temperature surveys and pressure fall off test to identify opportunities to optimize injection in some of the injectors. The findings from these activities enabled increasing injection rate up...

... Abstract In this work, we develop computationally efficient methods for deterministic production optimization under nonlinear constraints using a kernel-based machine learning method where the cost function is the net present value (NPV). We use the least-squares support-vector regression...

... confinement and storage capacity at each site through a thorough assessment. To assess the stability of the caprock after CO 2 injection, efficient tools are needed to evaluate the safe duration of CO 2 injection. This study used Particle Swarm Optimization (PSO) evolutionary algorithm to optimize...

... model by deep neural network is developed to predict the decline model variables and the ultimate total net power based on the reservoir parameters. The forward surrogate is integrated with a differential evolution optimizer, which considers reservoir uncertainties and nonlinear constraints...

... recovery method and G2L is optimized again. The strategies are entitled here as CW (WAG-CO 2 applied to G1 optimized for CIWG) and WC (CIWG applied to G1 optimized for WAG-CO 2 ). This activity can assess the need for pre-defining the recovery method before defining design variables, analyzing...

... computational cost than the single-porosity and single-permeability (SP/SP) approach. The time may be a limitation for cases that require many simulations, such as production optimization under uncertainty. This computational cost is more challenging when we couple DPDP models with compositional fluid models...

... Abstract As the oil and gas industry is continuously pushing boundaries of exploiting resources, it becomes more of a mandate to model and optimize forefront technologies. Multilateral wells are one example of a prevalent technology to maximize reservoir contact and return on investment...

... procedures may yield largely suboptimal decisions. In this work, we present a comprehensive outline for model-based life-cycle production optimization problems, establishing guidelines to make the process less subjective. Based on several applications and a literature review, we established a consistent...

... as well as a large operating range on the production and injection wells, making it an ideal candidate for streamline based water flood optimization measures. A multidisciplinary approach was taken to modelling the 16.TH, incorporating and analyzing data including pressures, production & injection...