Short-term Production Optimization Research Articles

Short-Term Production Optimization for Electric Submersible Pump Lifted Oil Field With Parametric Uncertainty

Short-Term Production Optimization for Electric Submersible Pump Lifted Oil Field With Parametric Uncertainty

Optimizing Operations Sequencing and Demulsifier Injection in Offshore Oil Production Platforms

Short-term production optimization of oil assets concerns routing decisions and equipment control settings that induce optimal steady-state operations. Typically, the decisions are made on the scale of hours to days with the aim of maximizing short-term economic and oil gains. Still an evolving field, this paper contributes by presenting a model and solution strategy for sequencing operations over time to drive the system towards an optimal state, while accounting for constraints that ensure smooth transitions until settling at the terminal steady-state. The constraints include the maximum number of wells that undergo a change in the control settings, maximum number and variation on the input signals for each well, and maximum number of changes in each time period, among others. Additionally, the proposed strategy can optimize the rate of injection of demulsifiers into wells considering the limits and impacts on fluid processing capacity. The paper reports simulated results for a real-world offshore production platform, demonstrating the effectiveness of the optimization strategy to cope with the failure and recovery of compression capacity, and manage the injection of demulsifiers.

Sequential short-term optimization of gas lift using linear programming: A case study of a mature oil field in Russia

The present study examines the sequential optimization of total lifting-gas allocation into multiple producing wells via linear programming for the maximization of oil production in a mature field located in Russia. Because the injection rate of lifting gas depends on the well production status, the ten-year gas lift schedule is determined by performing the gas lift optimization on a sequential, bi-weekly basis. For each short-term production optimization, the linear programming is solved by considering four operating parameters (e.g., tubing head pressure, bottom hole pressure, oil production rate, and gas lifting rate) which are calculated via high-fidelity reservoir simulation runs. Compared to the constant rate injection case, the optimal injection case increases the cumulative oil production by 14% at the same expenditure. These numerical results indicate that the sequential short-term optimization of the gas lift can be a practical approach towards boosting the asset value of a mature oil field with consideration for the well production status.

Introducing approximate well dynamics into production optimization for operations scheduling

Most of the literature on short-term production optimization concerns the computation of optimal system settings for steady-state operations. Such methodologies are applicable when the scales of time are faster than reservoir dynamics, and slower than the dynamics of top-side equipment. Effectively static problems are solved over time in response to changes in the prevailing conditions, which will remain persistent for long periods. However, when platform conditions change frequently or suddenly possibly due to reduced processing capacity, the dynamics of wells should not be neglected and well operations should be scheduled over time. To this end, this paper proposes a novel mathematical formulation for production optimization when dynamics matters, specifically when wells are shut-in (due to processing capacity drops) and restarted later as the normal conditions are recovered. The effectiveness of the methodology to schedule well operations is assessed by simulation of synthetic and field cases involving an offshore production platform.

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

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.

Constrained short-term and long-term multi-objective production optimization using general stochastic approximation algorithm

In the water flooding reservoir management, engineers aim to make an optimal production strategy to maximize both the net present value (NPV) in life-cycle and the short-term NPV. Previous researches of multi-objective optimization of long-term and short-term find that the short-term production optimization decreases the long-term NPV and the gradient calculation using adjoint gradient is hard to be applied. Therefore, we implement a multi-objective optimization method in which the long-term NPV is lumped into the short-term objective function as a constraint in order not to decrease the life-cycle NPV, linear and nonlinear constraints are lumped into the augmented Lagrangian objective function. Also, we propose a new gradient-free algorithm general stochastic approximation algorithm (GSA) to obtain the better approximate gradient. We proved that the research direction of GSA algorithm is always uphill, the simultaneous perturbation stochastic approximation algorithm (SPSA) and the ensemble-based optimization algorithm (EnOpt) are also two special forms of GSA algorithm. Toy problem results show that GSA has a more efficient approximation gradient than SPSA. Optimization results of a 2D reservoir show that the increase of the long-term NPV is about 86.5% and the increase of the short-term NPV is about 18.8%. The field case of Brugge field also shows that the long-term and short-term NPV increase by 64.7 and 48.9%.

Resilient Decision Making in Open Pit Short-term Production Planning in Presence of Geologic Uncertainty

Short-term production plans are the basis for operational mine production schedules. They concentrate on making long-term mine plans operationally feasible. Furthermore, some variables such as ore grade and tonnage govern mine production systems and cause uncertainty in the supply of raw materials to the mills. Due to the quality variation of material, short-term production optimization is an uncertainty-based problem. Feed quality is a prerequisite for the mill designer. It affects the mill efficiency and the type of measures with respect to environmental regulations. There is a need to control and to predict the quality of feed at the mine site to meet mill requirements, which is a complex problem. To deal with this issue a stochastic optimization model is developed to capture the effects of resource uncertainties on mine planning. In that regard, three performance indicators are defined and an optimized mining schedule is used to simulate the performance of these indicators throughout the mine-life. This will quantify the effects of geological uncertainty on short-term and long-term plans. The objectives of the model also include minimizing the deviation of expected quality and quantity from the required values. The methodology was illustrated using a case study on a surface gold mine in Iran. This approach leads to resilient decisions and better quality control.

Technology Focus: Production Operations (March 2014)

Technology Focus Production engineers are facing a new challenge when trying to select optimal well-production systems (in particular artificial lift) for important, relatively new resources: liquids-rich shale and deepwater subsalt applications. They are being asked to make such a selection when there are still large uncertainties about the well productivity and the produced-fluids thermodynamics (i.e., unclear functional requirements). These applications are overall quite different, and the reasons for the large uncertainties are not the same. On the other hand, both applications involve relatively deep wells and relatively high pressures and temperatures downhole and the associated complications. For both applications, one needs to try to select a system that is flexible, so that it can work properly in a relatively wide range of operating conditions; that is reliable, so that it does not require frequent interventions; and that is easily retrievable, so that there is no need to have an intervention with a full-size rig every time you have to replace a key part of the system. By the way, space is limited both in the casing downhole and in the tubing hanger (for extra penetrations); therefore, there is only so much you can install in the well to try to achieve all this. The recent increased cost and decreased availability of high-capacity rigs have certainly provided extra incentive for the development of rigless deployment and intervention techniques. Nevertheless, further technology development is still required to make available production and intervention systems that meet the demands of these applications. Meanwhile, what is a production engineer to do? The best approach will depend on the business drivers for the operator, in particular the right balance between shortand long-term goals. Some solutions may require a lower capital investment or allow for higher production rates initially but at the cost of a lower overall recovery from the reservoir in the long term. If the focus is on long-term reservoir recovery, the best approach likely will involve a higher capital investment and lower production rates (and lower drawdowns), at least initially. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 166177 Completion and Production Strategies for Liquids-Rich Wells in Ultralow-Permeability Reservoirs by Amit Kumar, Halliburton, et al. SPE 166027 Decision Analysis of Long-Term and Short-Term Production Optimization Applied to the Voador by Agus Hasan, Norwegian University of Science and Technology, et al. SPE 166379 A Case History: The Installation of a System To Restore Full Safety-Valve Functionality to a Malaysia Offshore Well With a Damaged Control Line by M.K.M. Aminuddin, Petronas, et al.

Use of Time-Varying Oil Price in Short-term Production Optimization for a Reservoir

Oil price significantly affects the economic performance of an oil field. In this study, Model Predictive Control (MPC) is used as a reference tracking method to provide corrective control action by meeting an objective, which balances short term and long term effects, based on an updated oil price. The long-term optimized production profile is based on a predicted oil price while the short-term objective is optimized with respect to the predicted short-term oil price by applying MPC. The fluid flow equations must be solved many times to find an optimal input for a short-term horizon; therefore, instead of a large-scale nonlinear reservoir model, local linear models are used to decrease the computation time for short-term optimization. The developed method is tested with several oil price scenarios on a synthetic case. The efficiency of the developed method is confirmed by comparing its economic performance to that using open loop long-term optimization.

Short-term production optimization of offshore oil and gas production using nonlinear model predictive control

The topic of this paper is the application of nonlinear model predictive control (NMPC) for optimizing control of an offshore oil and gas production facility. Of particular interest is the use of NMPC for direct short-term production optimization, where two methods for (one-layer) production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. Both methods can be implemented within standard NMPC software tools.The case-study first looks into the use of NMPC for ‘conventional’ pressure control, where disturbance rejection of time-varying disturbances (caused, e.g., by the ‘slugging’ phenomenon) is an issue. Then the above two methods for production optimization are employed, where both methods find the economically optimal operating point. Two different types of reservoir models are studied, using rate-independent and rate-dependent gas/oil ratios. These models lead to different types of optimums. The relative merits of the two methods for production optimization, and advantages of the two one-layer approaches compared to a two-layer structure, are discussed.

Real-time production optimization and reservoir management at the IO center

This paper presents research results within real-time production optimization and reservoir management at the Center for Integrated Operations in the Petroleum Industry (IO center). This includes life-cycle and long term issues like well location and target production rates. Further, results on short term production optimization, for instance the allocation of target production between wells and routing of wells into pipelines, will be presented. Finally, results on value chain optimization, where the production chain from reservoir to export is modelled, are reviewed.

Short-term Production Optimization of Offshore Oil and Gas Production Using Nonlinear Model Predictive Control

AbstractThis paper describes how nonlinear model predictive control (NMPC) can be used directly for short-term production optimization in an offshore oil and gas platform.Two methods for production optimization in NMPC are investigated. The first method is the unreachable setpoints method where an unreachable setpoint for oil production is used in order to maximize oil production. The ideas from this method are combined with the exact penalty function for soft constraints in a second method, named infeasible soft-constraints. These methods are used in a case study of offshore oil and gas production, where both methods find the economically optimal operating point. Their relative merits, and advantages compared to a two-layer structure, are discussed.

Hierarchical Long-Term and Short-Term Production Optimization

Summary Model-based dynamic optimization of oil production has a significant potential to improve economic life-cycle performance, as has been shown in various studies. However, within these studies, short-term operational objectives are generally neglected. As a result, the optimized injection and production rates often result in a considerable decrease in short-term production performance. In reality, however, it is often these short-term objectives that dictate the course of the operational strategy. Incorporating short-term goals into the life-cycle optimization problem, therefore, is an essential step in model-based life-cycle optimization. We propose a hierarchical optimization structure with multiple objectives. Within this framework, the life-cycle performance in terms of net present value (NPV) serves as the primary objective and shortterm operational performance is the secondary objective, such that optimality of the primary objective constrains the secondary optimization problem. This requires that optimality of the primary objective does not fix all degrees of freedom (DOF) of the decision variable space. Fortunately, the life-cycle optimization problem is generally ill-posed and contains many more decision variables than necessary. We present a method that identifies the redundant DOF in the life-cycle optimization problem, which can subsequently be used in the secondary optimization problem. In our study, we used a 3D reservoir in a fluvial depositional environment with a production life of 7 years. The primary objective is undiscounted NPV, while the secondary objective is aimed at maximizing shortterm production. The optimal life-cycle waterflooding strategy that includes short-term performance is compared to the optimal strategy that disregards short-term performance. The experiment shows a very large increase in short-term production, boosting first-year production by a factor of 2, without significantly compromising optimality of the primary objective, showing a slight drop in NPV of only -0.3%. Our method to determine the redundant DOF in the primary objective function relies on the computation of the Hessian matrix of the objective function with respect to the control variables. Although theoretically rigorous, this method is computationally infeasible for realistically sized problems. Therefore, we also developed a second, more pragmatic, method relying on an alternating sequence of optimizing the primary-and secondary-objective functions. Subsequently, we demonstrated that both methods lead to nearly identical results, which offers scope for application of hierarchical long-term and short-term production optimization to realistically sized flooding-optimization problems.

Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves

SummaryWe developed an operational strategy for commingled production with infinitely variable inflow control valves (ICVs) using sequential linear programming (SLP). The optimization algorithm requires instantaneous and derivative information. We propose a workflow in which the production engineer relies on measurements to determine the flow rate and pressure values and on models to determine the derivative information (i.e., the changes in flow rates as a result of a change in an ICV setting). Such a model typically would be a steady-state wellbore simulator including choke models to represent the ICVs and inflow models to represent the near-well reservoir flow in the various zones. The parameters of the model need to be updated regularly using real-time measurements and production tests, and we discuss the impact of different smart-well instrumentation levels on the updating process.We simulated the performance of this production-optimization strategy in a reservoir simulator. Some numerical aspects of the algorithm and problems encountered during implementation are discussed. The performance of the algorithm was tested in two reservoir settings. In both cases, the optimization resulted in accelerated oil production compared to conventional, surface-controlled production. However, accelerated production did not always result in higher ultimate recovery compared to the conventional case. In such situations, the benefits of either short-term production optimization (accelerating production) or long-term reservoir management (maximizing recovery) should be weighed.