Monitoring Deep Water Gulf of Mexico (DW GoM) wells with gravel-pack and frac-pack completions is an increasingly challenging task. Wells often experience increasing skin, adding to the risk of completion failure. Historically, sand control completions have experienced a 15% rate of sand related completion failures (King 2003). The industry tends to qualitatively evaluate safe target rates as skin increases. Reducing the flow rate based entirely on an increase in global skin can be too conservative and over-restrict target rate. Thus, it is important to know which components of the increase in skin can cause the completion to fail. Furthermore, it is not well understood how to quantify a safe target rate with the increased skin. This paper will present a new methodology to evaluate the components of skin increase which could cause sand control completions to fail. The failure mechanism we are addressing is perforations plugging by movement of fines and sand. Our new methodology helps quantify the risk and convert it into a safe target rate. This paper will also present case studies of oil and gas wells in the Na Kika Asset, in DW GoM where this methodology was successfully applied. The well completions are monitored with BP's flux based approach, (Tiffin 2003; Stein, Chitale, et al. 2005; Keck, et al. 2005). In all cases, the wells experienced increased skin, causing the engineers to choke back the well. The analysis showed that some of the skin increase was due to multiphase effects as the reservoir pressure was below the saturation pressure. Accounting for multiphase flow effects resulted in a 30% higher safe operating rate limit than with a conventional analysis. We also determined which skin components likely caused perforations plugging, thereby increasing the completion flux. The results allowed the Na Kika Asset to produce these wells at their maximum allowable safe operating rate with the higher skin, while producing within the BP's flux based guidelines. Introduction Setting a safe target rate for gravel packed DW GoM wells is a complex task of maximizing production while preserving the completion integrity. An industry study done by King (2003) showed a 15% failure rate in gravel packed completions. Operators control these wells using a maximum drawdown limit and generally over-constrain the production to avoid failures. Gravel packed and frac-packed wells have a tendency to build skin in and around the completion. It is believed that skin increase can cause completion damage. Before this study BP and the industry used a qualitative approach in determining the reduction in production necessary to mitigate the increased risk of well failure. Skin can increase due to various reasons such as fines migration, pseudo-skin due to relative permeability effects etc. Some of these skin components can be responsible for plugging the perforations and/or screens increasing the risk of failure and need to be accounted for in determining a safe target rate. We believe that some of the other skin components may not increase completion damage risk and can be discounted allowing favorable rate determination. To address this issue, BP set out to develop a quantitative relationship between failures and skin increase to improve upon the previously published flux based approach (Tiffin 2003). This work will present how the relevant components of skin can be used to correct the flux calculation.

An effective method is proposed for the solution of field development evaluation and optimization problems with categorical decision variables and subsurface uncertainties. Multiple reservoir models are applied for the representation of the subsurface uncertainties. A selected percentile of hydrocarbon recovery or an economic indicator (e.g. recovery factor, total recovery, or Net Present Value (NPV)) is maximized. The software package was developed for optimizing selections of: depletion scheme (e.g., primary depletion, gas injection, or water injection),facility size,well patterns and spacing,number of production or injection wells,well locations and trajectories from a discrete set of potential options,late-life sidetracks for existing and new wells,completion intervals for stacked reservoir units,order of reservoir zone development,well drilling schedules, etc. We consider problems with a limited number of potential values for each categorical decision variable. These combinatorial optimization problems are difficult to solve because the number of potential combinations is very large (factorial) and many hours of computer time are required for objective function evaluation running multiple reservoir models. We therefore apply a hybrid optimization method combining stochastic and sequential procedures. The sequential procedure is the novel part outlined in this paper. A reference depletion plan based on engineering experience is applied as an initial guess. Stochastic Genetic Algorithm (GA) or Particle Swarm Optimization (PSO) is executed until the objective function fails to improve in several iterations. Then, it is followed by the sequential procedure. The maximized objective function is the field oil recovery or economic indicator. The stochastic and sequential steps are repeated if the objective function is increased in the sequential procedure. The following operations are executed in each iteration of the sequential procedure: First, the previously evaluated development case with largest objective function value is identified as a starting point. Second, all potential values of the decision variables are evaluated sequentially changing one variable from the starting point. This field development optimization procedure was successfully applied in a large offshore oil field in the Gulf of Mexico (GOM). The field contains three major reservoir zones with high permeability and porosity. The field is structurally complex and it comprises of many fault blocks. We evaluated and optimized the waterflood performance and development drilling program selecting a) the numbers of new producers and injectors drilled in three reservoir zones; b) the locations and zonal completions of 41 new wells and sidetracks; and c) the well drilling schedule. NPV was maximized in the optimization procedure. The application of the hybrid method with the sequential procedure in the GOM oil field has the following benefits: The optimized field development case resulted in a 7% larger NPV and 3% larger oil recovery than those determined solely by GA.The individual well sensitivities on location and zonal completion were examined as part of the sequential procedure.The incremental value of each new well was determined.

Monitoring deepwater Gulf of Mexico (DW GOM) wells with gravel-pack and frac-pack completions is an increasingly challenging task. Wells often experience increasing skin, adding to the risk of completion failure. Historically, sand-control completions have experienced a 15% rate of sand-related completion failure (King et al. 2003). The industry tends to evaluate safe target rates qualitatively as skin increases. Reducing the flow rate entirely on the basis of an increase in global skin can be too conservative and can overrestrict target rate. Thus, it is important to know which components of the increase in skin can cause the completion to fail. Furthermore, it is not well understood how to quantify a safe target rate with the increased skin.This paper will present a new methodology to evaluate the components of skin increase that could cause sand-control completions to fail. The failure mechanism we address is perforation plugging by movement of fines and sand. Our new methodology helps to quantify the risk and convert it into a safe target rate. This paper will also present case studies of oil and gas wells in the Na Kika asset in DW GOM where this methodology was applied successfully.The well completions are monitored with BP's flux-based approach (Tiffin et al. 2003;Stein et al. 2005;Keck et al. 2005). In all cases, the wells experienced increased skin, causing the engineers to choke back the wells. The analysis showed that some of the skin increase was because of multiphase effects as the reservoir pressure was below the saturation pressure. We also determined which skin components likely caused perforation plugging, thereby increasing the completion flux. Accounting for multiphase-flow effects resulted in a higher safe-operating rate limit than with a conventional analysis. The results allowed the Na Kika asset to produce these wells at their maximum allowable safe-operating rate with the higher skin while producing within BP's flux-based guidelines.

This paper describes the results of a gas field development study that involved the integration of various technologies to determine how to lower field abandonment pressure, increase rate and reserves, improve profitability, and how to better manage the natural decline of the gas field. Subsurface and surface technologies were integrated. Part of the integration involved employing an integrated asset model. The integrated asset model (IAM) tool included a surface pipeline network simulator, coupled to wellbore models, reservoir inflow models, and material balance models for the entire field. This application addresses modeling of tight gas reservoirs with nodal analysis models in a manner that incorporates the transient aspects in the nodal analysis. The reservoir in this study had a high temperature, and thus water vapor was accounted for in the pressure drop calculations of both the wellbores and the surface pipelines. The modeling of compressors in the surface pipeline network model is also addressed. Modeling compressors with low suction pressure was found to be extremely challenging. Limitations and areas for improvement are discussed. Facility modifications to increase gas production, profitability, and gas reserves were identified. Better management of individual wells routed to particular compressors is expected to increase gas recovery by lowering overall field abandonment pressure. Introduction Gas reservoir depletion planning has become increasingly critical to effective allocation of manpower and material resources, and to maximizing production and reserves. It is especially important in this period of high natural gas prices in the U.S. An integrated asset model (IAM) is a useful tool to accomplish these goals. Optimization of compression utilization, and surface line modifications were the major options evaluated for improving financial and reservoir performance. An IAM was recently constructed in a mature Tight Formation Gas (TFG) reservoir, onshore Gulf of Mexico, to help determine the effect of various depletion scenarios on asset performance. The field described here was recently granted TFG severance tax relief (saving 7% of revenue) that is due to phase out in 2009. Clearly, the impetus is to maximize current rates while providing a depletion plan that maximizes recovery. As a result of this IAM tool, major changes are underway in the field to purchase 2680 HP of new low-low pressure (LLP- 25 psig) compression, reconfigure existing intermediate pressure (IP - 450 psig) compressors to low pressure (LP - 100 psig), and add approximately 4000 ft of 4" and 8" pipe to loop LP & LLP lines while maintaining the flexibility of placing individual wells into the optimum compression system. The IAM tool provides a holistic technical basis for these investment decisions and predicts rate and recovery gains of over 10% under the most economic depletion scenario evaluated (Figure 1). The Northeast Thompsonville gas field, located in Jim Hogg and Webb Counties Texas (Figure 2), produces approximately 25 MMscf/D from 32 wells. The producing intervals are completed in various sandstone reservoirs of the Wilcox formation. In the so-called 12,500 foot sand, initial reservoir pressure was 11,400 psig at 12,500 feet with reservoir temperature at 380°F. Water vapor in these high temperature reservoirs results in 10–20 BW/MMscf, depending on the reservoir pressure. The main reservoir is a dry gas reservoir, with no condensate production. Current gas recovery factors for various well drainage areas range from 50 to 95% of original gas-in-place. Effective permeabilities range from 20 microdarcies to 30 millidarcies with porosities in the 15 to 20% range. Most wells were stimulated with hydraulic fractures, typically with 300 to 400 ft effective fracture half-lengths and FcD's of about 1.

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThe Na Kika project, located in the deepwater Gulf of Mexico, is a unique development which ties-back six small to mediumsized oil and gas fields to the world's second deepest permanently moored production facility. Production from 12 subsea wells, in water depths ranging from 5800 to 7000 feet is routed to the production host through three flowline loops and one separate flowline. The project has been an economic and technological success. The application of intelligent well technology has enabled co-owners, Shell and BP, to successfully develop Na Kika with a minimum number of wells, and continues to help provide world-class reservoir surveillance data and ensure high standards of reservoir management.The fields are in a complex deepwater turbidite environment. Many of the reservoirs are highly faulted and compartmentalized due to salt movement, and several extend beneath salt structures and are difficult to image. Permanent downhole pressure and temperature sensors have been installed in all Na Kika wells. Additionally, four wells have been completed with interval control valves (ICVs) to enable 11 separate stacked reservoirs in two complex fields to be concurrently depleted.Intelligent well technology has provided dynamic performance data at the reservoir level that has been critical to improving reservoir characterization, reducing forecast uncertainty and enabled the operator to optimize production offtake. Downhole sensors provide well performance information such as permeability, skin and productivity index, on a real-time basis. Bottomhole pressure buildup (PBU) data are available on most well closures and have helped characterize reservoir barriers, zones of changing fluid mobility and levels of aquifer support. ICVs have enabled well testing at the reservoir level in multi-zone wells and have improved production allocation. This paper demonstrates how these surveillance data have been used to improve reservoir management and decision making.