... this framework are directly and quantitatively compared with the ones simulated using a commercial high-fidelity reservoir simulator. geology artificial intelligence neural network geologist optimization problem deep learning optimization constraint life-cycle production optimization nonlinearly...

... to the worst-case scenario where only 81% of the total injected H 2 was recovered. Whereas the total amount of injected H 2 in the worst scenario was around 25% of the optimal situation. geologist mineral sustainability optimization problem geology equation of state natural gas storage...

... 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...

... production while maintaining operational efficiency. By incorporating these constraints into the optimization problem, we can ensure that the optimized solution not only maximizes net present value but also satisfies the FLPR, FWPR, and WWPR constraints. (2c) c i ( u ) ≥ 0 ,   i = 1...

... geologist geology upstream oil & gas neural network enhanced recovery optimization problem reservoir geomechanics prediction fracture plane general physics-informed machine learning framework geological subdiscipline equation azizzadenesheli deep learning artificial...

... into the FDP optimization problem can be provided from a Bayesian perspective with the consideration of an ensemble of equiprobable realizations representing the subsoil, which can be updated with new data by eliminating the realizations that are proven wrong by the new data ( Walker et al. 2010 , Scheidt et...

...-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...

... with nonlinear state constraints. upstream oil & gas geology geologist optimization problem europe government machine learning artificial intelligence constraint water management united states government optimization lssvr proxy vector nonlinear constraint gradient simulation-based...

... & gas reserves evaluation geology geologist optimization problem reservoir neural network austria government deep learning europe government reservoir surveillance machine learning fracture vector constraint artificial intelligence production control geothermal reservoir simulation...

... the optimization plug-in in a commercial package. scenario optimization problem reservoir geologist united states government artificial intelligence geology asset and portfolio management optimization procedure optimizer field development optimization exhibition litvak technical conference...

... objective function sweetspot map drilling operation grid cell multilateral well optimization problem constraint optimization mathematical optimization maximum number cumulative oil production identification method scenario realization artificial intelligence upstream oil & gas input...

... on the bottom-hole pressure at the end of the history matching period in CSP. Nonlinearly Constrained Optimization Procedure The life-cycle production waterflooding optimization problem we consider is the estimation of the wells’ optimal controls that maximize the net present value (NPV), or equivalently...

... 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...

... that the overall NPV is higher than for an optimized water food. This indicates that a polymer flood could be beneficial compared to a water flood, even if geological uncertainties are not captured properly. enhanced recovery history matching machine learning optimization problem evolutionary algorithm...

... estimation production strategy fluid modeling asset and portfolio management optimization problem Upstream Oil & Gas RMO petroleum field development Risk analysis Scenario RMT risk curve risk and uncertainty assessment reservoir simulation EVOI representative model specialized strategy...

... to the number of objective functions. Artificial Intelligence Upstream Oil & Gas saturation data history matching objective function objective Sensitivity Study multi-objective optimization optimization problem constraint optimization Computation optimization process saturation objective...

... as well. machine learning Bayesian Inference chemical flooding methods optimization problem enhanced recovery objective function oil production experiment Upstream Oil & Gas concentration Chemical Injection saturation Artificial Intelligence Engineering Simulation simulation...

... Abstract Pipelines perform a critical function in the petroleum business by transporting hydrocarbon commodities between oil fields, refineries, and consumer markets. We formulate an optimization problem for determining the optimal pumping modes and flow configuration for liquid pipeline...

... approach is based on the adjoint gradient method for the constrained optimization problem. Relative permeability and capillary pressure curves, which are the objectives of history matching, within current implementation can have a variety of representations as Corey, LET, B-Splines and NURBS...

... Abstract Optimal field development and control aim to maximize the economic profit of oil and gas production. This, however, results in a complex optimization problem with a large number of correlated control variables at different levels (e.g. well locations, completions and controls...