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PRODUCTION OPTIMIZATION 18/279
. Equipment sizing, evaluation and selection such as Microsoft Excel and Visual Basic and features
. Daily production optimization, on-line or off-line ActiveX compliance.
. Problem and bottleneck detection/diagnosis
. Production forecasting
. Reservoir management 18.8.4.3 FAST Piper
. Data management FAST Piper (Fekete, 2001) is a gas pipeline, wellbore, and
reservoir deliverability model that enables the user to op-
Target and penalty functions are used in ReO within a timize both existing and proposed gas-gathering systems.
valid region. This type of ‘‘target’’ is required to find the FAST Piper is designed to be a ‘‘quick and simple looking
best compromise among conflicting objectives in a system. tool’’ that can solve very complicated gathering system
An example might be ensuring maximum production by designs and operating scenarios.
driving down wellhead pressure in a gas field while main- Developed and supported under Microsoft Windows
taining optimum intake pressures to a compressor train. 2000 and Windows XP, FAST Piper deals with critical
One of the most important aspects of modeling produc- issues such as multiphase flow, compressors, contracts,
tion systems is the correct calculation of fluid PVT prop- rate limitations, multiple wells, multiple pools, gas com-
erties. Variable detail and quality often characterizes the position tracking, among others. The Key Features FAST
PVT data available to the engineer, and ReO is designed to Piper include the following:
accommodate this. If complete compositional analysis
has been performed, this can be used directly. If only . Allows matching of current production conditions
Black Oil data are available, ReO will use a splitting . Analyzes ‘‘what-if’’ scenarios (additional wells, com-
technique to define a set of components to use in the pression, contracts, etc.)
compositional description. This approach means that dif- . Integrated the coal bed methane (CBM) reservoir model
ferent fluids, with different levels of detailed description allowing the user to predict the total gas and water
can be combined into the same base set of components. production of an interconnected network of CBM
Where wells are producing fluids of different composition, wells, while incorporating compressor capacity curves,
the mixing of these fluids is accurately modeled in the facility losses, and pipeline friction losses.
system. The composition is reported at all the nodes in
the network. This is highly valuable in fields with differing
wells compositions. 18.9 Discounted Revenue
The facility models available in ReO for gas networks The economics of production optimization projects is
include pipeline, chokes (both variable and fixed diam- evaluated on the basis of discounted revenue to be gener-
eter), block valves, standard compressors (polytropic ated by the projects. The most widely used method for
model), heat exchangers (intercoolers), gas and gas con- calculating the discounted revenue is to predict the net
densate wells, sinks (separators, gas export and delivery present value (NPV) defined as
points, flares, or vents), manifolds, links (no pressure loss
pipelines), and flanges (no flow constraint). NPV ¼ NPV R cost, (18:47)
Production constraints may be defined at any point where
within the production system in terms of pressure and/or m
flow rate along with objective functions for maximizing NPV R ¼ X DR n (18:48)
and minimizing flow rate or pressure in terms of sales ð 1 þ iÞ n ,
n¼1
revenues and costs.
ReO is seamlessly integrated with the program WellFlo where m is the remaining life of the system in years, and i is
application. WellFlo may be run from within ReO and the discount rate. The annual incremental revenue after
new well models may be defined or existing well models optimization is expressed as
used to simulate inflow and tubing performance. DR n ¼ $ðÞDN p, n , (18:49)
The most complex application of ReO has been in Latin
America where a network system including several hundred where ($) is oil or gas price and the DN p, n is the predicted
wells is optimized on a daily basis through a SCADA annual incremental cumulative production for year n,
system. This system includes a low-pressure gas-gathering which is expressed as
no
op
network integrated with a number of compressor trains and DN p, n ¼ N p, n N p, n , (18:50)
a high-pressure gas injection and distribution network.
where
N op ¼ forcasted annual cumulative production of
p, n
optimized system for year n
18.8.4.2 HYSYS no
HYSYS is an integrated steady-state and dynamic process N p, n ¼ predicted annual cumulative production of
simulator (AspenTech, 2005). HYSYS creates simulation non-optimized well for year n.
models for the following:
. Plant design Summary
. Performance monitoring This chapter presents principles of production optimization
. Troubleshooting of well, facility, and field levels. While well- and facility-
. Operational improvement level optimization computations can be carried out using
. Business planning Nodal analysis approach, field-level computations fre-
. Asset management quently require simulators with simultaneous solvers. Pro-
duction optimization is driven by production economics.
HYSYS offers an integrated set of intuitive and interactive
simulation and analysis tools and real-time applications. It
provides rapid evaluations of safe and reliable designs References
through quick creation of interactive models for ‘‘what
if’’ studies and sensitivity analysis. ahmed, t. Hydrocarbon Phase Behavior. Houston: Gulf
HYSYS Upstream is for handling petroleum fluids and Publishing Company, 1989.
RefSYS is for handling multiunit modeling and simulation AspenTech. Aspen HYSYS. Aspen Technology, Inc.,
of refinery systems. HYSYS interfaces with applications 2005.