Field-Development Optimization of the In-Situ Upgrading Process Including the Ramp-Up Phase

SPE Journal ◽  
2021 ◽  
pp. 1-16
Author(s):  
Faruk O. Alpak ◽  
Guohua Gao
Keyword(s):  
Steady State ◽  
Objective Function ◽  
Oil Recovery ◽  
Optimization Algorithm ◽  
Net Present Value ◽  
Performance Indicator ◽  
Field Scale ◽  
Field Development ◽  
Scale Surface

Summary Field-development optimization and optimization at the pattern scale are crucial to maximize the value of thermal enhanced-oil-recovery (EOR) projects. Application of a field net-present-value (NPV)-based pattern optimization algorithm honoring field-scale surface and subsurface constraints for in-situ-upgrading (IUP) projects has been described in the recent past. In this paper, we describe the development and application of a novel field-development-optimization capability, including the optimization of the ramp-up phase to accelerate the production to achieve a faster cash flow and high surface-facility utilization. We integrate this new capability into a robust field NPV optimization platform. A two-stagefield-development optimization algorithm is developed in this work. First, the steady-state pattern is optimized using the field-scale pattern optimization algorithm while honoring field-scale constraints and using a combined surface and subsurface performance-indicator-driven objective function. Ramp-up pattern designs are optimized separately using a solely pattern-scaleperformance-driven objective function in this stage. A preliminary pattern-delay time optimization follows next to precondition the problem for the subsequent field-scale optimization stage. The ramp-up pattern and pattern-delay times are optimized using a constant steady-state pattern in the second step of the algorithm. An appropriately penalized field-NPV-based objective function is used in this step to enforce field-scale surface and subsurface constraints. Optimization results on a realistic example application indicate that the time to oil-rate plateau could be significantly reduced on the order of multiple years while honoring the surface production constraints. This requires the use of an optimized ramp-up pattern in conjunction with the optimal steady-state pattern. The ramp-up pattern is approximately two patterns wide and features an increased heater density to deliver production acceleration. It is also notably more robust against the effects of subsurface uncertainties.

Feasibility Study on the Implementation of CO2-EOR Coupled with Sequestration in Trinidad via Reservoir Simulation

2014 ◽  
Author(s):  
J.. Narinesingh ◽  
D. V. Boodlal ◽  
D.. Alexander
Keyword(s):  
Primary Production ◽  
Oil Recovery ◽  
Net Present Value ◽  
Fluid Model ◽  
Gas Production ◽  
Economic Feasibility ◽  
Utilization Rate ◽  
Optimum Number ◽  
Field Development ◽  
Co2 Eor

Abstract The paper seeks to assess the technical and economic feasibility of implementing carbon dioxide enhanced oil recovery (CO2 EOR) in Trinidad and Tobago from flue gas production whilst mitigating the effect of greenhouse gases via CO2 sequestration. An existing power plant in Trinidad was selected as the CO2 source. As such, actual CO2 volumes and properties were found and used in this analysis. However, a hypothetical field was chosen as the appropriate sink, which can be analogous to a field in onshore Trinidad. A detailed reservoir model was built using the compositional fluid model CMG-GEM. Various scenarios were simulated to determine the optimum number of producers for primary production and the best location of the injectors for CO2 EOR. The optimum number of producers for the reservoir during primary production was found. In addition, the most favorable location of the injector to avoid early breakthrough and increase oil recovery was also determined. Many key parameters were reported from this investigation. These included OIIP, forecasted production and primary recovery. After primary production, CO2 EOR was then implemented with the use of the reservoir and fluid models and the additional recovery is reported. Other Key CO2-EOR parameters such as CO2 utilization rate and total sequestered CO2 were also quantified. Though a hypothetical reservoir was used, all associated data were defined and once an actual reservoir is known, the same technically rigid methodology can be applied. The OIIP was found to be 6.74 MMSTB for the selected reservoir. Based on an economic net present value (NPV) assessment, the optimum number of production wells for field development was found to be 3. At the end of primary production from these three wells (with 2.375 in. tubing), a total of 1.83 MMSTB were produced. This corresponded to a primary recovery factor of 27.2% over 4 years and 2 months. For CO2 EOR coupled with sequestration, these three wells were manipulated and used as 1 injector and 2 producers. CO2 EOR led to another 1.07 MMSTB of recovery for a total of 2.9 MMSTB (43.04% Recovery) for the ten year life of the project. A total of 5427 MMSCF (287 000 tons) of CO2 was sequestered in the reservoir (40.39% Storage) at an injection pressure of 1400 psi.

Multiobjective optimization of in situ process parameters in preparation of Al-4.5%Cu–TiC MMC using a grey relation based teaching–learning-based optimization algorithm

2017 ◽  
Vol 232 (4) ◽  
pp. 393-407 ◽  
Author(s):  
Biswajit Das ◽  
Susmita Roy ◽  
RN Rai ◽  
SC Saha
Keyword(s):  
Objective Function ◽  
Metal Matrix Composite ◽  
Parameter Optimization ◽  
Process Parameters ◽  
Optimization Algorithm ◽  
Metal Matrix ◽  
Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In modern in situ composite fabrication processes, the selection of optimal process parameters is greatly important for the preparation of best quality metal matrix composite. For achieving high-quality composite, an efficient optimization technique is essential. The present study explores the potential of a new robust algorithm named teaching–learning-based optimization algorithm for in situ process parameter optimization problems in fabrication of Al-4.5%Cu–TiC metal matrix composite fabricated by stir casting technique. Optimization process is carried out for optimizing the in situ processing parameters i.e. pouring temperature, stirring speed, reaction time for achieving better mechanical properties, i.e. better microhardness, toughness, and ultimate tensile strength. Taguchi’s L25 orthogonal array design of experiment was used for performing the experiments. Grey relational analysis is used for the conversion of the multiobjective function into a single objective function, which is being used as the objective function in the teaching–learning-based optimization algorithm. Confirmation test results show that the developed teaching–learning-based optimization model is a very efficient and robust approach for engineering materials process parameter optimization problems.

Maximization of a Dynamic Quadratic Interpolation Model for Production Optimization

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1012-1025 ◽  
Author(s):  
Hui Zhao ◽  
Chaohui Chen ◽  
Sy Do ◽  
Diego Oliveira ◽  
Gaoming Li ◽  
...  
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Net Present Value ◽  
Production Optimization ◽  
Petroleum Engineering ◽  
Interpolation Model ◽  
Quadratic Interpolation ◽  
Gradient Calculation ◽  
Mathematical Literature ◽  
Literature Production

Summary We derive and implement a new optimization algorithm on the basis of a quadratic interpolation model (QIM) for the maximization (or minimization) of a cost or objective function. Although we have also applied the algorithm in other petroleum-engineering applications, this paper restricts the algorithm's application to the production-optimization step of closed-loop reservoir management in which the objective function is the net present value (NPV) of production from a given reservoir. The new algorithm does not require a gradient calculation with an adjoint method but does use an approximate gradient (AG). Thus, the general optimization algorithm is referred to as QIM-AG. QIM-AG represents a significant modification of an optimization algorithm—new unconstrained optimization algorithm (NEWUOA)—derived fairly recently in the mathematical literature. Production-optimization examples show that QIM-AG results in a higher NPV in fewer iterations than is obtained with NEWUOA. QIM-AG is also compared with two other optimization algorithms that use an AG—namely, a slightly improved implementation of ensemble optimization presented in this paper and a new implementation of simultaneous perturbation stochastic approximation (SPSA).

scholarly journals Core-scale sensitivity study of CO2 foam injection strategies for mobility control, enhanced oil recovery, and CO2 storage

2020 ◽  
Vol 146 ◽  
pp. 02002
Author(s):  
Zachary Paul Alcorn ◽  
Sunniva B. Fredriksen ◽  
Mohan Sharma ◽  
Tore Føyen ◽  
Connie Wergeland ◽  
...  
Keyword(s):  
Steady State ◽  
Enhanced Oil Recovery ◽  
Oil Recovery ◽  
Co2 Storage ◽  
Sensitivity Study ◽  
Mobility Control ◽  
Wettability Alteration ◽  
Injection Strategy ◽  
Gravity Effects

This paper presents experimental and numerical sensitivity studies to assist injection strategy design for an ongoing CO2 foam field pilot. The aim is to increase the success of in-situ CO2 foam generation and propagation into the reservoir for CO2 mobility control, enhanced oil recovery (EOR) and CO2 storage. Un-steady state in-situ CO2 foam behavior, representative of the near wellbore region, and steady-state foam behavior was evaluated. Multi-cycle surfactant-alternating gas (SAG) provided the highest apparent viscosity foam of 120.2 cP, compared to co-injection (56.0 cP) and single-cycle SAG (18.2 cP) in 100% brine saturated porous media. CO2 foam EOR corefloods at first-contact miscible (FCM) conditions showed that multi-cycle SAG generated the highest apparent foam viscosity in the presence of refined oil (n-Decane). Multi-cycle SAG demonstrated high viscous displacement forces critical in field implementation where gravity effects and reservoir heterogeneities dominate. At multiple-contact miscible (MCM) conditions, no foam was generated with either injection strategy as a result of wettability alteration and foam destabilization in presence of crude oil. In both FCM and MCM corefloods, incremental oil recoveries were on average 30.6% OOIP regardless of injection strategy for CO2 foam and base cases (i.e. no surfactant). CO2 diffusion and miscibility dominated oil recovery at the core-scale resulting in high microscopic CO2 displacement. CO2 storage potential was 9.0% greater for multi-cycle SAGs compared to co-injections at MCM. A validated core-scale simulation model was used for a sensitivity analysis of grid resolution and foam quality. The model was robust in representing the observed foam behavior and will be extended to use in field scale simulations.

An Efficient Optimization Work Flow for Field-Scale In-Situ Upgrading Developments

SPE Journal ◽  
2015 ◽  
Vol 20 (04) ◽  
pp. 701-716 ◽  
Author(s):  
Guohua Gao ◽  
Jeroen C. Vink ◽  
Faruk O. Alpak ◽  
W.. Mo
Keyword(s):  
Net Present Value ◽  
Optimization Method ◽  
Training Data ◽  
Work Flow ◽  
Surface Model ◽  
Field Scale ◽  
Full Field ◽  
Single Pattern ◽  
Gradient Based

Summary In-situ upgrading process (IUP) is an attractive technology for developing unconventional extraheavy-oil reserves. Decisions are generally made on field-scale economics evaluated with dedicated commercial tools. However, it is difficult to conduct an automated IUP optimization process because of unavailable interface between the economic evaluator and commercial simulator/optimizer, and because IUP is such a highly complex process that full-field simulations are generally not feasible. In this paper, we developed an efficient optimization work flow by addressing three technical challenges for field-scale IUP developments. The first challenge was deriving an upscaling factor modeled after analytical superposition formulation; proposing an effective method of scaling up simulation results and economic terms generated from a single-pattern IUP reservoir-simulation model to field scale; and validating this approach numerically. The second challenge was proposing a response-surface model (RSM) of field economics to analytically compute key field economical indicators, such as net present value (NPV), by use of only a few single-pattern economic terms together with the upscaling factor, and validating this approach with a commercial tool. The proposed RSM approach is more efficient, accurate, and convenient because it requires only 15–20 simulation cases as training data, compared with thousands of simulation runs required by conventional methods. The third challenge is developing a new optimization method with many attractive features: well-parallelized, highly efficient and robust, and with a much-wider spectrum of applications than gradient-based or derivative-free methods, applicable to problems without any derivative, with derivatives available for some variables, or with derivatives available for all variables. This work flow allows us to perform automated field IUP optimizations by maximizing a full-field economics target while honoring all field-level facility constraints effectively. We have applied the work flow to optimize the IUP development of a carbonate heavy-oil asset. Our results show that the approach is robust and efficient, and leads to development options with a significantly improved field-scale NPV. This work flow can also be applied to other kinds of pattern-based field developments of shale gas and oil, and thermal processes such as steamdrive or steam-assisted gravity drainage.

scholarly journals Partially Separated Metamodels With Evolution Strategies for Well-Placement Optimization

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1003-1011 ◽  
Author(s):  
Z.. Bouzarkouna ◽  
D.Y.. Y. Ding ◽  
A.. Auger
Keyword(s):  
Objective Function ◽  
Net Present Value ◽  
Oil And Gas ◽  
Computational Cost ◽  
Computational Effort ◽  
Local Regression ◽  
Gas Field ◽  
Field Development ◽  
Reservoir Simulations ◽  
Partial Separability

Summary The net present value (NPV) of a project can be significantly increased by finding the optimal location of non-conventional wells. This optimization problem is nowadays one of the most challenging problems in oil-and gas-field development. Suitable methods to tackle this problem include stochastic optimization algorithms, which are particularly robust and able to deal with complex reservoir geology with high heterogeneities. However, these methods require in general a considerable computational effort in terms of number of reservoir simulations, which are CPU-time-demanding. This paper presents the use of the CMA-ES (covariance matrix adaptation—evolution strategy) optimizer, recognized as one of the most powerful derivative free optimizers, to optimize well locations and trajectories. A local-regression-based metamodel is incorporated into the optimization process in order to reduce the computational cost. The objective function (e.g., the NPV) can usually be split into local components, referring to each of the wells that moreover depends in general on a smaller number of principal parameters, and thus can be modeled as a partially separable function. In this paper, we propose to exploit the partial separability of the objective function into CMA-ES coupled with metamodels by building partially separated metamodels. Thus, different metamodels are built for each well or set of wells, which results in a more accurate modeling. An example is presented. Results show that taking advantage of the partial separability of the objective function leads to a significant decrease in the number of reservoir simulations needed to find the "optimal" well configuration, given a restricted budget of reservoir simulations. The proposed approach is practical and promising to deal with the placement of a large number of wells.

Alternative Method of Assessing Optimum Number of Water Injection and Infill Wells in a Field Development Plan

2021 ◽  
Author(s):  
M. Fitri Ramli ◽  
M. Shahrul M. Long ◽  
Amol Nivrutti Pote ◽  
Khairul Azri Ishak
Keyword(s):  
Oil Recovery ◽  
History Matching ◽  
Net Present Value ◽  
Water Injection ◽  
Numerical Approach ◽  
Development Plan ◽  
Alternative Methods ◽  
Optimum Number ◽  
Injection Wells ◽  
Field Development

Abstract This paper discusses the workflow and method of selecting optimum number of infill and injection wells based on incremental recovery. Normally, for infill wells study, a ‘creaming curve’ method is used to evaluate the optimum number of wells against incremental recovery from the field. However, in the case of determining number of infill wells together with water injection wells, a more comprehensive approach is needed. One needs to evaluate the pressure depletion rate from existing and infill wells together with the dynamic of the producer-injector pairings as well as the sweeping factor. The paper is based on infill and water injection development plan for a brown field in Sabah basin which located in Malaysia. To maintain operatability of the field in the future, several new infill and water injection wells options are evaluated for optimum field life oil production. Unlike infill or producer-only assessment, the same ‘creaming curve’ approach for combination of infill and water injection wells is less effective as large number of simulation runs are needed to sample the combination of these wells that generate optimum oil recovery. This has proven to be challenging especially when the models are large which is normally the case for brown fields and it requires extensive computational hours. In the first part, a modified approach bringing some pre-analytic assessment of producer-injector pairing is being used. The pairings are first ranked based on streamlines visualization, drainage tables and their respective contributions towards oil recovery. The ‘creaming curve’ is then built based on the highest contribution as well as the sequencing of the pairings. The second method mentioned in this paper is the numerical approach through multi-objective optimization using assisted history matching and uncertainty tool. With the help of optimizer, the number of simulation runs can be drastically reduced when only best combination of infills and injectors for each total number of wells are considered. Both alternative methods will be compared with the full computational runs, sampling every single combination of wells. Finally, the optimum number of wells with the combination of infill and water injection wells are analysed based on cumulative oil recovery against the Net Present Value (NPV). This case study therefore demonstrates how alternative methods can be used to resolve the optimum number of infill and water injection wells to avoid lengthy and very large numbers of simulation runs.

scholarly journals A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks

2019 ◽  
Vol 11 (19) ◽  
pp. 5283 ◽  
Author(s):  
Gowida ◽  
Moussa ◽  
Elkatatny ◽  
Ali
Keyword(s):  
Poisson’S Ratio ◽  
Accurate Determination ◽  
Oil And Gas Industry ◽  
Poisson's Ratio ◽  
Elastic Behavior ◽  
Percentage Error ◽  
Ann Model ◽  
Field Development ◽  
Well Completion

Rock mechanical properties play a key role in the optimization process of engineering practices in the oil and gas industry so that better field development decisions can be made. Estimation of these properties is central in well placement, drilling programs, and well completion design. The elastic behavior of rocks can be studied by determining two main parameters: Young’s modulus and Poisson’s ratio. Accurate determination of the Poisson’s ratio helps to estimate the in-situ horizontal stresses and in turn, avoid many critical problems which interrupt drilling operations, such as pipe sticking and wellbore instability issues. Accurate Poisson’s ratio values can be experimentally determined using retrieved core samples under simulated in-situ downhole conditions. However, this technique is time-consuming and economically ineffective, requiring the development of a more effective technique. This study has developed a new generalized model to estimate static Poisson’s ratio values of sandstone rocks using a supervised artificial neural network (ANN). The developed ANN model uses well log data such as bulk density and sonic log as the input parameters to target static Poisson’s ratio values as outputs. Subsequently, the developed ANN model was transformed into a more practical and easier to use white-box mode using an ANN-based empirical equation. Core data (692 data points) and their corresponding petrophysical data were used to train and test the ANN model. The self-adaptive differential evolution (SADE) algorithm was used to fine-tune the parameters of the ANN model to obtain the most accurate results in terms of the highest correlation coefficient (R) and the lowest mean absolute percentage error (MAPE). The results obtained from the optimized ANN model show an excellent agreement with the laboratory measured static Poisson’s ratio, confirming the high accuracy of the developed model. A comparison of the developed ANN-based empirical correlation with the previously developed approaches demonstrates the superiority of the developed correlation in predicting static Poisson’s ratio values with the highest R and the lowest MAPE. The developed correlation performs in a manner far superior to other approaches when validated against unseen field data. The developed ANN-based mathematical model can be used as a robust tool to estimate static Poisson’s ratio without the need to run the ANN model.

scholarly journals Practical CO2—WAG Field Operational Designs Using Hybrid Numerical-Machine-Learning Approaches

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1055
Author(s):  
Qian Sun ◽  
William Ampomah ◽  
Junyu You ◽  
Martha Cather ◽  
Robert Balch
Keyword(s):  
Machine Learning ◽  
Oil Recovery ◽  
History Matching ◽  
Optimization Problems ◽  
Learning Technologies ◽  
Petroleum Engineering ◽  
Support Vector ◽  
Learning Approaches ◽  
Field Development ◽  
Proxy Models

Machine-learning technologies have exhibited robust competences in solving many petroleum engineering problems. The accurate predictivity and fast computational speed enable a large volume of time-consuming engineering processes such as history-matching and field development optimization. The Southwest Regional Partnership on Carbon Sequestration (SWP) project desires rigorous history-matching and multi-objective optimization processes, which fits the superiorities of the machine-learning approaches. Although the machine-learning proxy models are trained and validated before imposing to solve practical problems, the error margin would essentially introduce uncertainties to the results. In this paper, a hybrid numerical machine-learning workflow solving various optimization problems is presented. By coupling the expert machine-learning proxies with a global optimizer, the workflow successfully solves the history-matching and CO2 water alternative gas (WAG) design problem with low computational overheads. The history-matching work considers the heterogeneities of multiphase relative characteristics, and the CO2-WAG injection design takes multiple techno-economic objective functions into accounts. This work trained an expert response surface, a support vector machine, and a multi-layer neural network as proxy models to effectively learn the high-dimensional nonlinear data structure. The proposed workflow suggests revisiting the high-fidelity numerical simulator for validation purposes. The experience gained from this work would provide valuable guiding insights to similar CO2 enhanced oil recovery (EOR) projects.

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