Luis E. Zerpa

Hydrate Risk Assessment and Restart-Procedure Optimization of an Offshore Well Using a Transient Hydrate Prediction Model

October 2012 • Oil and Gas Facilities 49 Summary A produced-hydrocarbon stream from a wellhead encounters formation of solid gas-hydrate deposits, which plug flowlines and which are one of the most challenging problems in deep subsea facilities. This paper describes a gas-hydrate model for oil-dominated systems, which can be used for the design and optimization of facilities focusing on the prevention, management, and remediation of hydrates in flowlines. Using a typical geometry and fluid properties of an offshore well from the Caratinga field located in the Campos basin in Brazil, the gas-hydrate model is applied to study the hydrate-plugging risk at three different periods of the well life. Additionally, the gas-hydrate model is applied to study the performance of the injection of ethanol as a thermodynamic hydrate inhibitor in steady-state flow and transient shut-in/restart operations. The application of the transient gas-hydrate model proved to be useful in determining the optimal ethanol concentration that minimized the hydrate-plugging risk.

An optimization methodology of alkaline-surfactant-polymer flooding processes using field scale numerical simulation and multiple surrogates

After conventional waterflood processes the residual oil in the reservoir remains as a discontinuous phase in the form of oil drops trapped by capillary forces and is likely to be around 70% of the original oil in place (OOIP). The EOR method socalled alkaline–surfactant–polymer (ASP) flooding has proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through the reduction of interfacial tension and mobility ratio between oil and water phases. A critical step to make ASP floodings more effective is to find the optimal values of design variables that will maximize a given performance measure (e.g., net present value, cumulative oil recovery) considering a heterogeneous and multiphase petroleum reservoir. Previously reported works using reservoir numerical simulation have been limited to sensitivity analyses at core and field scale levels because the formal optimization problem includes computationally expensive objective function evaluations (field scale numerical simulations). This work presents a surrogate-based optimization methodology to overcome this shortcoming. The proposed approach estimates the optimal values for a set of design variables (e.g., slug size and concentration of the chemical agents) to maximize the cumulative oil recovery from a heterogeneous and multiphase petroleum reservoir subject to an ASP flooding. The surrogate-based optimization approach has been shown to be useful in the optimization of computationally expensive simulation-based models in the aerospace, automotive, and oil industries. In this work, we improve upon this approach along two directions: (i) using multiple surrogates for optimization, and (ii) incorporating an adaptive weighted average model of the individual surrogates. The cited approach involves the coupled execution of a global optimization algorithm and fast surrogates (i.e., based on Polynomial Regression, Kriging, Radial Basis Functions and a Weighted Average Model) constructed from field scale 0920-4105/

An Efficient Response Surface Approach for the Optimization of ASP Flooding Processes: ASP Pilot Project LL-03 Reservoir

s doi:10.1016/j.pe T Correspondi E-mail addr ngineering 47 (2005) 197–208 ee front matter D 2005 Elsevier B.V. All rights reserved. trol.2005.03.002 ng author. Tel.: +58 261 7598630; fax: +58 261 7598411. ess: nqueipo@ica.luz.ve (N.V. Queipo). L.E. Zerpa et al. / Journal of Petroleum Science and Engineering 47 (2005) 197–208 198 numerical simulation data. The global optimization program implements the DIRECT algorithm and the reservoir numerical simulations are conducted using the UTCHEM program from the University of Texas at Austin. The effectiveness and efficiency of the proposed methodology is demonstrated using a field scale case study. D 2005 Elsevier B.V. All rights reserved.

A model for the integrated optimization of oil production systems

The EOR method so called alkaline-surfactant-polymer (ASP) flooding has proved to be effective in reducing the oil residual saturation in laboratory experiments and field projects through the reduction of interfacial tension and mobility ratio between oil and water phases. Two issues are critical for a successful ASP flooding project: i) addressing issues related to optimization of the laboratory design, and ii) establishing an optimal injection scheme for the field scale flooding process. This paper presents an efficient solution approach for the latter issue. The approach is based on the construction of quadratic response surface models (surrogates) of reservoir simulator outputs and three-level D-optimal design of experiments. It allows to effectively and efficiently establish the optimum ASP injection scheme, and was applied to determine the optimal values of injection rates, slug size and initial date for injection of an case study at pilot project level. The optimum injection scheme resulted in substantial savings in chemicals used when compared to the laboratory design.

An optimization methodology of alkaline–surfactant–polymer flooding processes using field scale numerical simulation and multiple surrogates

Typically, the optimization of oil production systems is conducted as a non-systematic effort in the form of trial and error processes for determining the combination of variables that leads to an optimal behavior of the system under consideration. An optimal or near optimal selection of oil production system parameters could significantly decrease costs and add value. This paper presents a solution methodology for the optimization of integrated oil production systems at the design and operational levels, involving the coupled execution of simulation models and optimization algorithms (SQP and DIRECT). The optimization refers to the maximization of performance measures such as revenue present value or cumulative oil production as objective functions, and tubing diameter, choke diameter, pipeline diameter, and oil flow rate as optimization variables. The reference configuration of the oil production system includes models for the reservoir, tubing, choke, separator, and business economics. The optimization algorithms Sequential Quadratic Programming (SQP) and DIRECT are considered as state-of-the-art in non-linear programming and global optimization methods, respectively. The proposed solution methodology effectively and efficiently optimizes integrated oil production systems within the context of synthetic case studies, and holds promise to be useful in more general scenarios in the oil industry.