Summary
Well-developed methodology exists for handling uncertainty for a single
reservoir. However,development of multiple fields presents a significant
challenge when uncertainty in a large number of variables, such as gas in place
and liquid yield, occur in each reservoir. Some of the challenges stem from our
need to forecast the system behavior involving a coupled
reservoir/wellbore/surface (CRWS) network for the entire spectrum of variables
so that facilities can be designed for the range of fluid composition and
throughput. Of course, assessing well count and sequencing well drills are some
of the important objectives.
This paper describes probabilistic production forecasting with a
compositional CRWS network model for nine reservoirs involved in delivering gas
supply to a liquefied natural gas (LNG) plant in Nigeria. Our main objective
was to use an economic indicator to select the optimal design of two main
pipelines, each transporting 200 and 300 MMscf/D from the two production
platforms, located 15 and 5 km, respectively, from the processing platform.
Rate and cumulative profiles showed that sustained deliverability of gas
could be realized for approximately 11 years before the decline occurred in
high-permeability reservoirs. In other words, uncertainty in gas in place did
not surface during the plateau period, only during the decline period lasting
another 5 years after the first 11. In contrast, the liquid rates exhibited a
large uncertainty band throughout, a direct manifestation of the condensate
yield issue. The uncertainty band among each of the 12 components aided
facilities design. Differences in net present value (NPV) and discounted
profitability index (DPI) were used as discriminators for discerning optimal
pipe size from the standpoint of project economics.
Introduction
In recent years, probabilistic forecasting has gained popularity and has
become the preferred approach when assessing the value of a project, given the
uncertainty of many input variables. Uncertainties arise because both static
and dynamic variables are ascertained from rather small volumetric samples of a
reservoir and subsequent key variables are estimated from interpretations.
Systematic approaches have emerged to account for uncertainty of both static
and dynamic variables involving statistical approaches. These methods have been
detailed elsewhere (Damsleth et al. 1992; Friedmann et al. 2003; Kabir et al.
2004) for a single reservoir. However, very few studies exist in which
production is sought from multiple reservoirs with uncertainty associated with
each one of them. Cullick et al. (2004) and Narayanan et al. (2003) have
presented case studies of production forecasting under uncertainty for multiple
fields. In their studies, flow-simulation tools were integrated with economic
evaluation tools and the Monte Carlo (MC) algorithm. Optimization was sought
for an objective function (NPV, for instance) honoring various constraints.
The objective of this study was to investigate the impact of uncertainty in
input variables on the production forecast for systems consisting of multiple
gas/condensate reservoirs, honoring wellbore constraints. We studied multiple
reservoirs with multiple wells producing independently. The complexity arises
because of the interactions through the common flowline system. The wellbore
model was coupled with the reservoir model to honor wellbore constraints. The
surface network interfaced with disparate wells through producing rules or
constraints. Some of the producing rules included production upper limits to
avoid erosional velocity and meeting CO2 production constraints because
blending of various streams occurs. In this study, the types of uncertainty
considered are in-place volume, condensate yield, capital costs, and operating
costs.
We segmented this study into two phases. In Phase 1, we used an analytic
simulator to generate the pressure and production forecasts for dry-gas
reservoirs, coupled with a simple economic model but without the surface
network. The intrinsic idea was to establish well count with a simplistic
approach on a spreadsheet. In Phase 2, a CRWS model allowed us to discern the
pipe diameter of two main trunk lines transporting gas/condensate fluids by use
of incremental economics.
© 2007. Society of Petroleum Engineers
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History
- Original manuscript received:
12 July 2005
- Revised manuscript received:
13 November 2006
- Manuscript approved:
27 November 2006
- Version of record:
20 June 2007
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