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.
© 2007. Society of Petroleum Engineers
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History
- Original manuscript received:
29 August 2005
- Meeting paper published:
1 November 2005
- Revised manuscript received:
15 August 2006
- Manuscript approved:
10 October 2006
- Version of record:
20 February 2007
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