Summary

Giant, geologically complex heavy-oil fields can take decades to develop, so development decisions made early in the life of the field can have long-range implications. Decision and risk analysis (D&RA) is often needed to make decisions that will maximize the risk-adjusted economic benefit. Unfortunately, in large fields, D&RA can be very challenging because of the large number of variables and the endless number of development and expansion scenarios to analyze. The time needed to complete a D&RA can become prohibitive when full-field reservoir simulation is the main tool for forecasting primary production and well count, with one simulation taking many hours or days to complete.

This paper describes two new methods developed to overcome these challenges for a specific depletion-drive heavy-oil reservoir: a method for optimally populating a model with hundreds of horizontal wells, and a method to optimize expansion decisions quickly and directly. The utility of these tools has not been determined for other reservoirs and/or recovery mechanisms.

A semiautomated spreadsheet-and-simulation method was developed to quickly place and select hundreds to thousands of hypothetical/future horizontal wells in a multimillion-gridblock model. Because the method automatically accounted for all model static properties and their effects on dynamic production response, the hypothetical wells had productivity characteristics very similar to the actual drilled wells placed in the model.

A multivariate nonlinear interpolation method was developed that enabled full-field forecasts—for any combination of acreage allocation, well count, drilling order, and field rate constraint—to be calculated in less than 5 seconds, compared to approximately 20 hours for traditional simulation. Extensive validation work showed that well count and production curves from the spreadsheet virtually overlaid those obtained using traditional simulation of the particular expansion scenario. Such close agreement was possible because the basis of the spreadsheet forecast was utilization of traditional simulation forecasts from a handful of relevant cases.

A key breakthrough beyond just fast forecasting was the coupling of the following three components inside the same spreadsheet: the fast forecasting method, calculation of an economic indicator/objective function (NPV), and commercial optimization tools. This linkage made possible, perhaps for the first time (at least at this scale), realization of direct optimization of any development scenario in a matter of minutes to a few hours, depending on the number of variables being optimized.

Introduction

The field in question was a giant extra heavy-oil accumulation covering hundreds of square miles and containing billions of barrels of 7 to 9ºAPI gravity oil trapped in shallow (1,500 to 3,000 ft) sandstone reservoirs of Miocene age (Fig. 1). The major reservoir sands were deposited in fluvial and fluviotidal channel systems. Reservoir properties were excellent, with porosity values of up to 36% and permeability values of up to 30-40 darcies. The gross interval was divided into three independent reservoir intervals by thick shales and further subdivided into a total of 12 sands. The variations in depth and oil gravity resulted in variations in pressure, temperature, solution gas/oil ratio (GOR), and oil viscosity (in-situ live-oil viscosity ranged from 1,000 to 10,000 cp). An upgrader was built to partially refine the crude.

The upgrader capacity limited maximum production rate, and the contract term limited the production duration; combined, these defined the maximum that could be produced under the project scope. Whether this maximum would be achieved was contingent on drilling sufficient wells to fill the upgrader for the whole term. The ultimate number of wells required would depend on the performance of these wells, which in turn would depend on their locations, the reservoir and oil quality encountered, and the operating constraints imposed by artificial lift methods, pipeline pressures, and facility capacities.

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