Robust formulations for production optimization of satellite oil wells

Hülse, Eduardo Otte; Camponogara, Eduardo

doi:10.1080/0305215x.2016.1211128

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…The non-linear well and flowline performances are approximated using SOS2 piecewise linear models. This approach is similar to what other authors have presented in the past, e.g., , , Silva and Camponogara (2014), Hulse and Camponogara (2017), and Silva et al (2015). Hoffmann et al (2016) successfully apply this technique to optimize downhole diluent injection for an offshore heavy oil field and forecast field production.…”

Section: Introductionsupporting

confidence: 89%

Optimized production profile using a coupled reservoir-network model

Hoffmann¹,

Stanko

González

2019

J Petrol Explor Prod Technol

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This paper presents an integrated approach for scheduling and forecasting oil and gas production by integrating models of the entire value chain, from the reservoirs to the sales points. The methodology ensures maximum oil production at each time step of the reservoir simulator while honoring all operational constraints of the system. The proposed method is applied to a small North Sea offshore field consisting of two oil reservoirs with API gravities of 37 and 39. 3 gas-lifted wells are producing in each reservoir. They are arranged in a production network connected to a surface process. Control variables include individual well choke opening (early stage) and gas lift injection rate (later stage). The system is subject to numerous operational constraints (e.g., maximum field liquid production, maximum gas lift injection rate). The proposed solution is built in a commercial IAM platform that connects the models and orchestrates the software execution and optimization. The optimization problem is formulated as a Mixed Integer Linear Program. The well and flowline performance curves are approximated with piecewise linear functions. Results show that such an integrated approach can significantly affect the production profile (up to 15% difference against traditional "silo" approach). The proposed integrated solution is two-to-three times faster than traditional non-linear optimization methods, guarantees convergence towards the global maximum and it represents with an appropriate level of accuracy the original black-box model. This allows to run a lot of different scenarios making it a suitable tool for field development and planning optimization. The proposed method is used to optimize the field design and schedule. Optimal surface capacities are determined by brute force exploration of net present value function.

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Section: Introductionsupporting

confidence: 89%

Optimized production profile using a coupled reservoir-network model

Hoffmann¹,

Stanko

González

2019

J Petrol Explor Prod Technol

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“…Codas et al [7] develop a MILP framework for integrated production optimization of complex oil fields in Brazil with routing of wells, limited processing capacity, pressure constraints, wells with gas-coning behavior and wells sharing flowlines. The work of Codas and Camponogara [8], Silva and Camponogara [9], Hulse and Camponogara [10] and Silva et al [11] are other examples of successful applications of piecewise linear approximations to oil & gas production optimization. More recently, Hoffmann and Stanko [12] apply this technique to optimize a large production network with ESP-boosted wells.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Model-Based Production Optimization for a Gas Field in North Africa

Hoffmann¹

2018

J. Model. Optim.

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This paper presents a model-based optimization solution suitable for short-term production optimization of large gas fields with wells producing into a common surface network into a shared gas treatment plant. The proposed methodology is applied to a field consisting of one dry gas reservoir with a CO2 content of 7.3% and one wet gas reservoir with a CO2 content of 2.8% and initial CGR of 15 stb/MMscf. 23 wells are producing, and all gas production is processed in a common gas treatment plant where condensates and CO2 are extracted from the reservoir gas. The final sales gas must honor compositional constraints (CO2 content and heating value). The proposed solution consists of a bi-level optimization algorithm. A Mixed Integer Linear Programming (MILP) formulation of the optimization problem is solved, assuming some key parameters in the gas plant to be constant. Hydraulic performances of the system, approximated using SOS2 piecewise linear models, and condensates and CO2 extraction, captured using simplified models, are included in the MILP. After solving the MILP, the values of the key parameters are calculated using a full simulation model of the gas plant and the new values are substituted in the MILP input data. This iterative procedure continues until convergence is achieved. Results show that the proposed methodology can find the optimum choke openings for all wells to maximize the total gas rate while honoring numerous surface constraints. The solution runs in 30 sec. and an average of 3-4 iterations is needed to achieve convergence. It is therefore a suitable solution for short-term production optimization and daily operations.

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“…Since then, optimization for the worst case value of the parameters within a set has become effectively known as Robust Optimization. A static robust optimization approach for gas lift well optimization using the robust counterpart formulation, as described in [3], was recently presented in [16]. However, since the uncertainty is simple in the considered problem, the worst case can be easily determined a priori without explicitly formulating the robust counterpart.…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

“…A two-stage optimization formulation for production optimization under uncertainty, which defines an operational strategy rather than a single operating point, was suggested in [15] and was demonstrated using static models. Very recently, the production optimization problem was reformulated as a robust optimization problem following the row-wise and column-wise framework with cardinality constraints in [16], where a level of protection against the uncertainty is sought at the cost of conservativeness.…”

Section: Previous Workmentioning

confidence: 99%

Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

2016

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Abstract:In this work, we consider the problem of daily production optimization in the upstream oil and gas domain. The objective is to find the optimal decision variables that utilize the production systems efficiently and maximize the revenue. Typically, mathematical models are used to find the optimal operation in such processes. However, such prediction models are subject to uncertainty that has been often overlooked, and the optimal solution based on nominal models can thus render the solution useless and may lead to infeasibility when implemented. To ensure robust feasibility, worst case optimization may be employed; however, the solution may be rather conservative. Alternatively, we propose the use of scenario-based optimization to reduce the conservativeness. The results of the nominal, worst case and scenario-based optimization are compared and discussed.

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Robust formulations for production optimization of satellite oil wells

Cited by 11 publications

References 24 publications

Optimized production profile using a coupled reservoir-network model

Optimized production profile using a coupled reservoir-network model

Short-Term Model-Based Production Optimization for a Gas Field in North Africa

Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

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