For some petroleum fields, optimization of production operations can be a major factor for increasing production rates and reducing production costs. While for single wells or other small systems simple nodal analysis can be adequate, large complex systems demand a much more sophisticated approach. Many mature fields are produced by gas-lift under multiple constraints imposed by the field handling capacity of the system. In this paper we present an optimization technique for allocating production rates and lift-gas rates to wells of large fields subject to multiple flow rate and pressure constraints. The well rate and lift-gas rate allocation problem has been addressed in the literature1–13. However, existing methods are either inefficient or make significant simplifications. This often leads to suboptimal operations. This paper proposes a new formulation of the problem that is able to handle flow interactions among wells and can be applied to a variety of problems of varying complexities. We show that the proper formulation of the optimization problem is important in the practical use of modern optimization techniques. Once formulated, the optimization problem is solved by a sequential quadratic programming algorithm. Our results show that the procedure developed in this paper is capable of handling complex oil production problems. Introduction In petroleum fields, hydrocarbon production is often constrained by reservoir conditions, deliverability of the pipeline network, fluid handling capacity of surface facilities, safety and economic considerations, or a combination of these considerations. While production can be controlled by adjusting well production rates, allocating lift-gas rates, and in some fields, by switching well connections from one flowline to another flowline, implementation of these controls in an optimal manner is not easy. The objective of dynamic production optimization is to find the best operational settings at a given time, subject to all constraints, to achieve certain operational goals. These goals can vary from field to field and with time. Typically one may wish to maximize daily oil rates or minimize production costs. Various aspects of production optimization have been addressed in the literature. For example, several researchers1–5 have studied the problem of allocating limited amount of available gas to specified wells for continuous gas-lift. Fang and Lo6 proposed a linear programming technique to allocate lift-gas and well rates subject to multiple flow rate constraints. Barnes et al.7 developed an optimization technique for a portion of the Prudhoe Bay field in Alaska. This model maximizes oil production while minimizing the need for gas processing. Several papers8–10 have reported results for the production optimization of the Kuparuk River field in Alaska. The techniques published so far1–10 either addressed only a part of the optimization problem of interest to us or made significant simplifications during the optimization process. In most commercial reservoir simulators11,12, flow rate constraints on facilities are handled sequentially by ad hoc rules. In addition, gas-lift optimization is done separately from the allocation of well rates. Because of the nonlinear nature of the optimization problem and complex interactions, results from such procedures can be unsatisfactory. In a companion paper Wang et al.13 presented a procedure for the simultaneous optimization of well rates, lift-gas rates, and well connections subject to multiple pressure, flow rate, and velocity constraints. While this approach was successful, it was limited in its ability to handle flow interactions among wells when allocating well rates and lift-gas rates. Here we extend the work of Wang et al.13 and propose a new formulation for the problem of simultaneously optimizing the allocation of well rates and lift-gas rates. The optimization problem is solved by a sequential quadratic programming algorithm, which is a derivative-based nonlinear optimization algorithm. The proposed method is tested on several examples. Results show that the method is capable of handling flow interactions among wells and can be applied to a variety of problems of varying complexities and sizes.
SPE Members Abstract An integrated reservoir, well tubing string, and surface pipeline network model of the Prudhoe Bay oil field has been constructed. The integrated model incorporates a new procedure for the simultaneous solution of the reservoir and surface pipeline network flow equations. It also includes an optimization technique to allocate well production rates. As a result of the effectiveness of the developed procedures, the new technology for integrated reservoir and surface facility modeling has been successfully applied to a facility optimization study of the giant Prudhoe Bay oil field. Introduction Business Motivation. Production from the Prudhoe Bay oil field is on decline. For this reason, optimal usage of the surface facilities is a major factor in reducing production costs. Along with other measures, production costs for Prudhoe Bay can be reduced by–defining the optimum surface facility structure and operating conditions (optimum number of separator stages and their connections, optimum separator pressure, etc.);–using any excess capacity in the Prudhoe Bay surface facilities to process third party fluid production from satellite oil fields. However, changes to the surface facility system will impact production from Prudhoe Bay wells. For example, tubinghead pressures for some Prudhoe Bay wells will increase if production from the satellite fields is conveyed to the separators via the existing surface pipeline network system. In this case, production rates for these wells would be reduced. Tools. Integrated compositional models of–the reservoir,–well tubing strings,–the surface pipeline network system,–separator banks, and central gas facility are constructed to evaluate the impact of facility modifications on well production profiles. The compositional reservoir model and its history matching will be described in a separate paper. The integration of the central gas facility and reservoir models is presented in Reference 1. In this paper, we describe the well tubing string and surface pipeline network models and their integration with the reservoir model. Model Objectives. The integrated reservoir and surface pipeline network model provides the capability to–allocate production well rates in a reservoir simulation from pressure constraints at the separator banks and from surface facility limits,–define optimum well assignments to high or low pressure flowline and separation systems–determine the impact of surface facility changes on a production profile. Normally, tubinghead pressure or bottomhole pressure is used for the well rate allocation in reservoir simulations. However, well tubinghead (or bottomhole) pressures change in time as a result of well gas-oil ratio and water cut variations, and these changes are difficult to predict. Challenges. Construction of an integrated model of the reservoir and surface pipeline network system for the Prudhoe Bay oil field is a very challenging and difficult problem for the following reasons: P. 435^
Understanding effective fracture length and characterizing drainage patterns is critical for optimal development of unconventional resources. This paper documents a comprehensive field experiment in the Bakken formation, where several fracture diagnostic technologies and drainage mapping methods were used in a unique project setup to measure effective fracture length and map drainage. Two vertical wells (V1 and V2) were drilled 1,000 ft away from a Bakken lateral (H1) with 10 years of production (Fig. 1). The two vertical wells were 200 ft apart. V2 was used for microseismic and deformation (downhole tilt) measurements, while V1 was used for pressure measurements and hydraulic fracture characterization. The project consisted of re-pressurizing the existing lateral (parent well), using microseismic monitoring to map drainage (designated MDD, Dohmen et al. 2013, 2014, 2017). A DFIT was performed in the V1 well before the MDD to measure local stress and pore pressure. Following the MDD, a small propped fracture treatment was pumped in the V1. The H1 well was then produced for 4 months and DFITs pumped in the V1 and V2 wells. This comprehensive fracture diagnostic dataset was integrated with detailed core and log measurements, hydraulic fracture modeling, and advanced reservoir simulation to characterize the hydraulic fracture performance. The H1 MDD indicated that the major pressure depletion (drainage) was approximately 500 ft on either side of the lateral. H1 BHP showed local reservoir pressure was 1,000 psi. The initial V1 DFIT showed virgin reservoir pressure, but surprisingly, the 20 bbl DFIT injection was detected on the H1 BHP gauge. Microseismic mapping of the V1 fracture treatment (15 klbs, 600 bbl) showed a planar fracture with a half-length of 1,000 ft. The V1 fracture "hit" the H1, measured by microseismic and confirmed by a 1,650 psi increase in H1 BHP. The microseismic showed a symmetrical fracture, suggesting that the H1 re-pressurization mitigated the detrimental effects of parent well depletion that can cause severe asymmetry. Downhole tilt and microseismic showed fracture height quickly extended downward through the lower Bakken shale into the Three Forks. The V1 was not produced. However, the H1 oil rate doubled after the V1 frac hit, indicating significant stimulation. V1 DFIT #2 showed 1,800 psi depletion, while the V2 DFIT showed approximately 1,000 psi depletion, confirming that the V1 fracture is "flowing" into the H1 lateral 1,000 ft away. The reservoir simulation history match indicated low fracture conductivity, but enough to improve well productivity and drain oil over 1,000-ft from the H1 lateral. This paper details a comprehensive fracture diagnostic dataset gathered in a unique field laboratory where a single hydraulic fracture from a vertical well is used to characterize fracture conductivity and flowing length.
A new optimization technology has been demonstrated for giant oil and gas-condensate fields. Because of the very large number of wells involved, optimization is carried out on well patterns, well spacing in patterns, order of drilling different zones, etc. Earlier developed technology for the optimization of well placement, drilling schedule, well trajectories, and injection strategy has been extended for the optimization of field development schemes involving a very large numbers of wells. The powerful optimization procedure is based on a global optimization technique such as the Genetic Algorithm and other mixed integer optimizers. Potential field development schemes that match field development constraints are identified. A global optimizer is then applied to determine the optimum field development scheme relative to pre-defined economic criteria and analysis. A statistical proxy procedure is utilized to reduce the number of optimization iterations and computing time required to solve this difficult optimization problem. Economic indicators (e.g. net present value, incremental rate of return, capital efficiency, discounted payback, etc.) or oil/gas recovery are maximized subject to field development constraints. The multiple reservoir models representing uncertainties in reservoir geology are handled within the workflow and are represented in predicted results. The applications of this optimization technology is clearly demonstrated to be a powerful and sufficiently general tool for analysis of most real world field development planning issues. Significant improvements (3–13 %) in oil/gas recovery and net present value are demonstrated in the optimum cases when compared with the best manually achieved reference field development case. Introduction One of the critical issues facing an owner, investor or operator of an oil or gas accumulation is the definition of the "optimal" development plan to maximize the expected recovery of hydrocarbons or financial profits from production of their hydrocarbon accumulation. The choices associated with development are great, including items such as: the well type, horizontal or vertical, wellbore length within any given interval, wellbore trajectory, completion technology, e.g., (cased and perforated, slotted linear, gravel packed), well placement, well spacing, producer to injector ratio, drilling order, production and injection rate limits, pattern type, oil, water and gas production and facility limits, type and amount of artificial lift, etc. These choices when coupled with the uncertainty in the subsurface characterization such as: structural depth of pay, compartmentalization via faults or stratigraphic discontinuity, variability in the distribution of porosity, permeability, rock type and saturation, etc very quickly lead to a daunting number of potential development designs that require evaluation in order to determining an optimum development plan. Even with advances in today's reservoir and facility simulation technologies and computer hardware, most investigators when faced with such a large number of complex possibilities adopt a manual or semi-manual method of analysis and selection of the design which they then assert is optimum. Typically tens, rather than hundreds of designs are actually fully simulated and investigated. From these results a final design is selected.
Field development optimization is a computationally intensive task due to the large number of reservoir simulation runs required. These simulations can be expensive, especially for large and complex reservoir models. Proxies can be used to efficiently estimate the objective function value for new scenarios and can act to reduce the number of simulations required. Thus they can be very useful for speeding up field development optimization. In this paper a procedure that combines an optimization algorithm (in this case a genetic algorithm or GA) and a new statistical proxy is described. The statistical proxy has the following key elements. First, a new selection procedure called individual-based selection is applied to decide which individuals (scenarios) are to be simulated. Second, the new approach uses multiple proxies for optimization problems involving multiple reservoir models, which are needed to account for geological uncertainty. Third, the statistical proxy is modified to work efficiently in distributed computing environments. Finally, the proxy procedure is successfully incorporated into an existing general field development optimization package (Williams et al., 2004; Litvak et al., 2007a). In the individual-based selection method, for each scenario the proxy estimate of the objective function is compared to a threshold. If the estimate exceeds the threshold, then the case is simulated (otherwise it is not simulated). The threshold corresponds to a specified percentile of the cumulative distribution function constructed from previously simulated cases and therefore changes during the course of the optimization. In cases with multiple reservoir models, each model has its own corresponding proxy. This eliminates the problem of duplicate objective function estimates for different reservoir models, which may occur with previous proxy-based methods. The individual-based selection method is shown to perform better for a particular example than the population-based method published previously. The overall procedure is applied to the optimization of infill drilling where we maximize the incremental net present value (NPV) by optimizing new well locations, well type and rig schedule, subject to field development constraints. We demonstrate the capabilities of the proxy using synthetic reservoir models and a real field in the Gulf of Mexico. In the first example, two optimization cases are considered, corresponding to the use of single and multiple reservoir models. In the case with one reservoir model, the hybrid procedure found the same field development scenario compared to GA only, and required 85% fewer simulations. In the case with multiple reservoir models, the hybrid procedure found a slightly different field development scenario than the pure GA approach, though the NPV from the hybrid procedure was within 1% of that using only GA. The hybrid approach, however, required 91% fewer simulations for this case. In the field application, a better field development scenario with 45% fewer simulations was found using the hybrid algorithm (GA and proxy) compared to using only GA. These examples clearly demonstrate the effectiveness of the statistical proxy procedure for accelerating field development optimization.
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