The Pains and Gains of Experimental Design and Response Surface Applications in Reservoir Simulation Studies

2009 ◽  
Author(s):  
Chidi Amudo ◽  
Thomas Graf ◽  
Rashmin R. Dandekar ◽  
James M. Randle
Keyword(s):  
Experimental Design ◽  
Response Surface ◽  
Reservoir Simulation ◽  
Simulation Studies

Efficient Design of Reservoir Simulation Studies for Development and Optimization

2007 ◽  
Vol 10 (06) ◽  
pp. 629-637 ◽  
Author(s):  
Subhash Kalla ◽  
Christopher David White
Keyword(s):  
Experimental Design ◽  
Reservoir Simulation ◽  
Computer Experiments ◽  
Orthogonal Arrays ◽  
Economic Factors ◽  
Development Studies ◽  
Simulation Studies ◽  
Gas Reservoir ◽  
Response Models ◽  
Production Plans

Summary Development studies examine geologic, engineering, and economic factors to formulate and optimize production plans. If there are many factors, these studies are prohibitively expensive unless simulation runs are chosen efficiently. Experimental design and response models improve study efficiency and have been widely applied in reservoir engineering. To approximate nonlinear oil and gas reservoir responses, designs must consider factors at more than two levels—not just high and low values. However, multilevel designs require many simulations, especially if many factors are being considered. Partial factorial and mixed designs are more efficient than full factorials, but multilevel partial factorial designs are difficult to formulate. Alternatively, orthogonal arrays (OAs) and nearly-orthogonal arrays (NOAs) provide the required design properties and can handle many factors. These designs span the factor space with fewer runs, can be manipulated easily, and are appropriate for computer experiments. The proposed methods were used to model a gas well with water coning. Eleven geologic factors were varied while optimizing three engineering factors. An NOA was specified with three levels for eight factors and four levels for the remaining six factors. The proposed design required 36 simulations compared to 26,873,856 runs for a full factorial design. Kriged response surfaces are compared to polynomial regression surfaces. Polynomial-response models are used to optimize completion length, tubinghead pressure, and tubing diameter for a partially penetrating well in a gas reservoir with uncertain properties. OAs, Hammersley sequences (HSs), and response models offer a flexible, efficient framework for reservoir simulation studies. Complexity of Reservoir Studies Reservoir studies require integration of geologic properties, drilling and production strategies, and economic parameters. Integration is complex because parameters such as permeability, gas price, and fluid saturations are uncertain. In exploration and production decisions, alternatives such as well placement, artificial lift, and capital investment must be evaluated. Development studies examine these alternatives, as well as geologic, engineering, and economic factors to formulate and optimize production plans (Narayanan et al. 2003). Reservoir studies may require many simulations to evaluate the many factor effects on reservoir performance measures, such as net present value (NPV) and breakthrough time. Despite the exponential growth of computer memory and speed, computing accurate sensitivities and optimizing production performance is still expensive, to the point that it may not be feasible to consider all alternative models. Thus, simulation runs should be chosen as efficiently as possible. Experimental design addresses this problem statistically, and along with response models, it has been applied in engineering science (White et al. 2001; Peng and Gupta 2004; Peake et al. 2005; Sacks et al. 1989a) toMinimize computational costs by choosing a small but statistically representative set of simulation runs for predicting responses (e.g., recovery)Decrease expected error compared with nonoptimal simulation designs (i.e., sets of sample points)Evaluate sensitivity of responses to varying factorsTranslate uncertainty in input factors to uncertainty in predicted performance (i.e., uncertainty analysis)Estimate value of information to focus resources on reducing uncertainty in factors that have the most significant effect on response uncertainty to help optimize engineering factors.

scholarly journals Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

Membranes ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 70
Author(s):  
Jasir Jawad ◽  
Alaa H. Hawari ◽  
Syed Javaid Zaidi
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Forward Osmosis ◽  
Feed Solution ◽  
Operating Conditions ◽  
Ann Model ◽  
Membrane Flux ◽  
Input Variables ◽  
Rsm Model

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

Adsorption heat pump optimization by experimental design and response surface methodology

2018 ◽  
Vol 138 ◽  
pp. 849-860 ◽  
Author(s):  
Joana M. Pinheiro ◽  
Sérgio Salústio ◽  
Anabela A. Valente ◽  
Carlos M. Silva
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Heat Pump ◽  
Adsorption Heat ◽  
Adsorption Heat Pump

Optimization and experimental design by response surface method for reactive extraction of glutaric acid

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Anuj Kumar ◽  
Pranay Mohadikar ◽  
Fiona Mary Anthony ◽  
Diwakar Z. Shende ◽  
Kailas L. Wasewar ◽  
...  
Keyword(s):  
Experimental Design ◽  
Response Surface ◽  
Glutaric Acid ◽  
Extraction Efficiency ◽  
Fermentation Broth ◽  
Maximum Efficiency ◽  
Surface Model ◽  
Environment Friendly ◽  
Independent Variables ◽  
Reactive Separation

Abstract Glutaric acid is an attractive chemical compound which can be used for the manufacturing of polyesters, polyamides, and polyols. It can be produced by the synthesis (chemical method) and fermentation (biological method) process. Glutaric acid is presented with the lowest quantity in the fermentation broth and industrial waste streams. The separation methods of glutaric acid are difficult, costly, and non-environment friendly from fermentation broth. Reactive separation is a simple, cheapest, and environment-friendly process for the recovery of carboxylic acid. Which can be employed for the separation of glutaric acid with lower cost and environment-friendly process. In this study, response surface methodology (RSM) was used as a mathematical technique to optimize and experimental design for investigation of the reactive separation of glutaric acid from the aqueous phase. As per RSM study, 20 experiments with different independent variables such as concentration of glutaric acid, % v/v of trioctylamine, and pH for recovery of glutaric acid were performed. The optimum condition with maximum efficiency (η) 92.03% for 20% trioctylamine and pH = 3 at 0.08 mol/L of glutaric acid initial concentration were observed. The lower concentration of trioctylamine provides sufficient extraction efficiency of glutaric acid. This method can also be used for the separation from fermentation broth because a lower concentration of trioctylamine which makes this process environment-friendly. The optimization condition-defined quadratic response surface model is significant with R 2 of 0.9873. The independent variables defined the effect on the extraction efficiency of glutaric acid. This data can be used for the separation of glutaric acid from industries waste and fermentation broth.

OPTIMIZATION OF ELECROSPINNING OF PVDF SCAFFOLDS FABRICATION USING RESPONSE SURFACE METHOD

2015 ◽  
Vol 75 (6) ◽  
Author(s):  
Madana Leela Nallappan ◽  
Mohamad Mahmoud Nasef
Keyword(s):  
Experimental Design ◽  
Response Surface ◽  
Vinylidene Fluoride ◽  
Fibre Diameter ◽  
Solution Concentration ◽  
Operating Conditions ◽  
Optimum Operating ◽  
Statistical Validation ◽  
Electric Voltage ◽  
Average Fibre Diameter

Poly(vinylidene fluoride) (PVDF) scaffolds were prepared via electrospinning. The response surface methodology (RSM) was used to optimize the parameters that influence the average fibre diameter. The objective is to produce fibres with small diameters. The factors considered for experimental design were the applied electric voltage, the PVDF solution concentration, and the distance between the needle tip and the collecting drum. The Central Composite Design (CCD) was used to generate the experimental design whilst the analysis of variance (ANOVA) was performed to obtain statistical validation of regression models and to study the interaction between input parameters. The optimum operating conditions that guaranteed PVDF scaffolds with small nanofibre diameter were in the voltage and concentration range of 16-20 kV and 10-14wt%.

Preparation and Optimization of Capsaicin-Loaded Chitosan Nanoparticles as Delivery Systems Using Box-Behnken Design

2020 ◽  
Vol 998 ◽  
pp. 277-282
Author(s):  
Narissara Kulpreechanan ◽  
Feuangthit N. Sorasitthiyanukarn
Keyword(s):  
Response Surface Methodology ◽  
Experimental Design ◽  
Response Surface ◽  
Functional Foods ◽  
Water Solubility ◽  
Chitosan Nanoparticles ◽  
Tween 80 ◽  
Limiting Factors ◽  
Design And Optimization ◽  
Chili Peppers

Capsaicin (CAP) is a pungent alkaloid of chili peppers that is obtained from chili peppers that has a variety of pharmacological activities and can be used in various areas, such as functional foods, nutritional supplements and medical nutrition. Capsaicin has important anticancer, antioxidant and anti-inflammatory properties that allow to be applied as treatment for several diseases. However, its lack of water solubility, as well as its poor oral bioavailability in biological systems, show limiting factors for its successful application. Recently, the formulation of capsaicin for food and pharmaceutical use is limited. Therefore, the present study emphasized on preparation of capsaicin-loaded chitosan nanoparticles (CAP-CSNPs) and design and optimization of the formulation using Box-Behnken experimental design (BBD) and response surface methodology (RSM). The capsaicin-loaded chitosan nanoparticles were prepared by o/w emulsification and ionotropic gelification. The optimized formulation of capsaicin-loaded chitosan nanoparticles had a chitosan concentration of 0.11 (%w/v), a Tween 80® concentration of 1.55 (%w/v) and a CAP concentration of 1 mg/mL and that it should be stored at 4°C. Box-Behnken experimental design and response surface methodology was found to be a powerful technique for design and optimization of the preparation of capsaicin-loaded chitosan nanoparticles using limited number of experimental runs. Our study demonstrated that capsaicin-loaded chitosan nanoparticles can be potentially utilized as dietary supplements, nutraceuticals and functional foods.

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