The E-M field is a gas reservoir that has been under production for nearly a decade. This paper presents the effort of the team to revise and improve the sub-surface model to delineate new drilling targets. Closure of the field to the east was uncertain, but critical to the field development plan. Inversion of the seismic data created an absolute acoustic impedance cube and a derived effective porosity cube. Attributes were extracted from each of the various seismic data, using direct and interval extractions following the interpreted surfaces. Careful inspection of the seismic amplitude in cross section revealed a flat spot, indicating a potential fluid contact. This feature was confirmed in several of the extracted attributes which were then used to constrain an iterative depth conversion. Fault interpretations in time were then adjusted to match the new depth horizons creating enechelon faults in a fashion analogous to surface outcrops observed in the Cape Town region.Uncertainty also exists with respect to the vertical isolation of the reservoirs. Log responses record a thin shale, below seismic vertical resolution, in all of the drilled wells. However, the areal extent of the shale is unknown, and vertical communication is a possibility. Stochastic representations of the potential shale extents were introduced into the dynamic fluid-flow simulation, and fed through an experimental design to test the impact on vertical connectivity. Using a proxy model from these results a Monte-Carlo simulation provided a probability distribution for the production response.The methods presented here are applicable to fields wherever sharp acoustic impedance contrasts exist between fluid types, particularly in gas reservoirs or reservoirs with an existing gas cap. Seismic attributes have refined the depth conversion for the E-M field, and the resulting geomodel provides new drilling targets for the operator.

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Well placement decisions are routinely made on the basis of simulation models that are created before production operations begin. Real-time downhole pressure data and surface flow rate information can provide a significant set of calibration information early in the life of the reservoir. In this paper we describe a method for comparing a set of assumed reservoir parameters, especially the presence of a connected aquifer and its size, with a set of simulation models to assist with well placement decisions. In the South Timbalier 316 block, a delineation well penetrated the steeply dipping B4 reservoir near the oil/water contact. Based on a comparison of downhole pressure data, with data from simulation models, the operator concluded that a connected aquifer was present and estimated its size. This information was sufficient for the operator to know that the well would not be needed as a water injector and to justify a sidetrack from the downdip location to an updip location. When the updip sidetrack well was drilled, reservoir rock quality was below the minimum for a commercial completion. This brought into question the viability of any hydrocarbon storage capacity in the northern portion of the field. As soon as the updip sidetrack well was logged, a "what-if" reservoir model was run to simulate a no-hydrocarbon-reservoir scenario in the northern portion of the field. This reduction represented approximately 25% of the reservoir hydrocarbon pore volume. The model results clearly indicated that this was not a reasonable model and gave the operator confidence to sidetrack the well directly to the west, to a slightly downstructure position, whereby a successful completion was made. Without this "quick-response reservoir model" the well may have been sidetracked to the south, resulting in a less-than-optimal well location. Introduction The implementation of permanent downhole pressure gauges (PDGs) has provided a new source of highly valuable pressure transient data. In this paper we demonstrate a method to maximize the value of that information through the appropriate application of pressure transient analysis (PTA) and its incorporation into a full-field numerical simulation model. A practical application of this technology is demonstrated with an example from the South Timbalier 316 field. The South Timbalier 316 field is located in U.S. Federal waters offshore Louisiana in the Gulf of Mexico. The discovery well, A1, was drilled into the distal end of a deepwater turbidite fan. The turbidite mass transport phenomenon provided for the elimination of fines, leaving a blocky, unconsolidated sandstone as the reservoir. Between thicker sand-dominated depositional events a fine shale layer was deposited that separates the Upper B4 from the Lower B4 reservoir horizon. The original mini-basin appears to have been uplifted by deeper salt intrusion, leaving the present reservoir at a very steep 45°slope. The reservoir is highly overpressured, exhibiting a pressure gradient of 0.7 psi/ft. Real-Time Data Transmission During the completion design process, the operator decided to introduce one PDG in each of the final wells (Fig. 1). A high-resolution quartz gauge was positioned on the exterior of the tubing string and exposed to wellbore pressure by a port; then it was connected to surface by shielded wiring, which provides a continuous readout of gauge pressure once every second. At the surface, these data are temporarily stored on a computer, the Acquisition Surface Unit (ASU) (which connects the input data streams from all the various surface and downhole sources), and are transmitted via satellite to shore at 15-sec intervals. Once on shore, the data are verified at the Data Management Center. On occasion, due to weather or other events, the data stream is interrupted. The data management engineers can then retrieve the missing data from the ASU and repair the interrupted interval. The data are routinely archived and backed up to multiple locations for data security.

We present a framework that automatically generates an optimal well placement plan (WPP) based on a reservoir model. The proposed WPP comprises wells, their completions, and the drilling schedule. A suite of high-speed computational components allows this WPP to be generated in minutes. Greenfields and brownfields are supported. Brownfields require consideration of historical and ongoing production by existing wells along with collision avoidance when proposing new wells. The proposed wells can be producers or water injectors with vertical, deviated, or horizontal geometries. Different development strategies can be investigated that allow targets to be driven by geology or standard pattern such as an inverted five-spot. In addition to proposing new wells, existing wells may be sidetracked or recompleted. Optimization of the WPP uses a constrained downhill simplex approach. During a trial, WPPs proposed by the optimizer in earlier trials are extrapolated to propose a new WPP. The proposed WPP must satisfy a wide range of geometric, operational, contractual, and legal constraints on the surface and in the overburden and reservoir. Collision and hazard avoidance computation uses a geocomputation topology approach. When a feasible WPP is discovered, the production forecast is computed using a high-speed semi-analytical reservoir simulator, which renders a result within a few seconds, using an analytically computed pressure and explicitly computed saturation. This reservoir simulator is fully three-dimensional and discretizes the reservoir to represent the underlying heterogeneity. In addition to recovery, the framework allows a variety of objective functions including net present value, return on investment, and profitability index. Optimization in the presence of subsurface uncertainty is considered by using an ensemble of reservoir models. A proposed WPP will then have an uncertainty in the forecast value. For a specified aversion to risk, a conservative or aggressive WPP can then be optimized. The framework has been applied to a variety of workflows. These include rapid evaluation of the potential of different waterflooding strategies, drilling multiple infill wells from existing platforms, and identification of sidetrack candidates in mature fields. This new framework has many applications in the field development planning workflow, including rapid screening of multiple fields and development scenarios. The most promising scenarios can be used with detailed numerical simulation for further validation.