About 70% of the world's oil and gas fields are brownfields with production history of 25 years or older. These makes additional recovery from brownfields a focus of interest for the industry. A handful of known technologies exists which have been applied to brownfield redevelopments in a timely and efficient manner to maximize oil recovery. In this paper, we present an innovative approach to rejuvenate brownfields which relies on the application of direct search methods to place, design and selectively perforate multi-pattern infill wells in unexploited hydrocarbon regions while considering multiple history-matched models and geological uncertainties. The field development optimization (FDO) approach requires the definition of design parameters for efficient well placement and selective perforation in untapped sweet spots using I-J-K coordinates. Fluid saturation distribution from a finite difference reservoir simulator at the end of production history is utilized to identify unexploited regions. Sampling techniques are applied to design parameters to screen the solution space for the best candidates that satisfies the FDO problem. Optimization of selected best candidates are performed using the genetic algorithm (GA), level-set optimizer (LSO) and the covariance matrix adaptation evolution strategy (CMA-ES). In addition, we take into consideration the influence of geological uncertainties on multi-pattern well placement and oil recovery maximization. A partially real field model is used to test proposed FDO approach. Oil recovery maximization and overall optimization time is compared for all three optimization methods evaluated. Using fluid saturation distribution realized after model validation, favourable unexploited regions are identified for multi-pattern well placement by computing and inspecting the product of permeability-by-thickness-by-fluid-saturation per grid block. Optimization algorithms applied to the FDO problem show the tendency for multi-pattern infill wells to seek untapped regions of the reservoir. Furthermore, we parameterize infill well perforations to control the drainage strategy in our test model. Results show that the optimization of perforations delivered better final solutions. Optimization under uncertainty has an effect on the maximization of oil recovery with lesser effect observed on optimum well placement.
The potential of CEOR application in mature oil fields can be investigated using sector models of an appropriate boundary conditions. In this paper, we present an evaluation of feasibility study of surfactant and estimation of the incremental recoverable oil in a mature Libyan oil field assuming the availability of surfactant formulation with optimal performance at reservoir conditions. Overall permeability of reservoir rock is rather low which limits the applicable areas of CEOR applications. Reservoir properties were characterized using an established optimization approach to define pilot areas that exhibit favorable conditions for chemical EOR flooding. An intensive study was accomplished to generate a sector model of an optimum boundary conditions that provide pronounced results to the Full Field Model (FFM). Typical laboratory data were used to design surfactant model at an ultra-low interfacial tension (IFT) of 10−3 mN/m. Furthermore, main parameters that could influence the results of surfactant model were optimized: flow rates, residual oil saturation (Sorc), correlated Capillary De-Saturation Curve (CDC), adsorption, and grid size effect. Interstitial velocity of displacing fluid and capillary number were correlated to describe the effect of permeability variation on the ultimate residual oil saturation. Additional recovery by surfactant at current reservoir conditions appeared to be strongly affected by changing the correlated CDC. The estimated macroscopic efficiency of surfactant by the coarse and fine grid models indicates that the surfactant is being smeared in the coarse model, and consequently different pressure distribution in both models was observed after certain time of injection. Moreover, the predicted results illustrate the influence of any heterogeneity feature in reservoir properties on both microscopic (ED) and macroscopic (EV) sweep efficiencies of CEOR flooding. In Addition to the lessons learned of proper simulation at field scale, a developed approach to evaluate the potential of CEOR at challenging reservoir conditions is introduced in this paper.
Reservoir simulation workflows from history to prediction are built on a number of alternative optimization and sampling techniques with different characteristics. Adjoint techniques derive analytical sensitivities directly from the flow equations of the simulator. In a model update step those sensitivities are used for property modifications on grid cell level. Derivative-free optimization techniques like evolutionary algorithms are flexible and deliver alternative history matched cases. The propagation of reservoir uncertainties from history to prediction is often investigated using ensemble-based approaches. Markov Chain Monte Carlo and EnKF techniques are applied to generate an approximate posterior distribution as a basis for estimating prediction uncertainties. In this work we investigate alternative optimization, sampling and data assimilation techniques with application to history matching workflows within one application framework for comparison. Validated simulation models are used for production forecast and field development planning. Methods are investigated with a focus on history matching efficiency, flexibility to integrate different types of model parameters, integration option for static and dynamic modeling workflows, capability of supporting uncertainty quantification workflows for estimating prediction uncertainties and the ability to leverage on distributed computing. Optimization workflows are applied to a reasonably complex benchmark problem for comparison. Well production data is used in the model calibration phase and history matched simulation models are carried forward to prediction. The impact of alternative optimization concepts on estimating prediction uncertainties is discussed. This work gives an overview on alternative optimization concepts and relates capabilities, strengths and weaknesses. The impact of workflow choices on estimating prediction uncertainties is discussed. Practical conclusions are drawn for real field applications scenarios.
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