American Oil and Gas Reporter - January 2015 - 164

FIGURE 5
Verification of 11 Months of Production Forecast Results
(Well 05112324430)
Model's History Match

Model's Prediction

Actual Data

800

2013 Data
Model's Prediction
for year 2013

Oil Production (bbl/month)

700
600
500
400
300
200
100
0
Jun-07

Oct-08

Mar-10

Jul-11

Dec-12

Date (month)

S. ALIREZA
HAGHIGHAT

S. Alireza Haghighat is a reservoir engineer at Eclipse Resources in Pennsylvania. He joined the company last fall,
after receiving a Ph.D. in petroleum and
natural gas engineering from West Virginia University. At WVU, Haghighat's
research was focused on applying artificial intelligence and data mining in
smart wells, carbon dioxide sequestration modeling, and production analysis
of unconventional reservoirs.

VIDA
GHOLAMI

Vida Gholami is a research associate
in the Petroleum and Natural Gas Engineering Department at West Virginia University. During the past seven years, she
has worked on multiple research projects
related to reservoir modeling and simulation, and applying artificial intelligence
and data mining in the upstream industry. Gholami holds a B.S. in petroleum
engineering from the Petroleum University of Technology, and an M.S. and a
Ph.D. in petroleum and natural gas engineering from West Virginia University.

SHAHAB D.
MOHAGHEGH

Shahab D. Mohaghegh is a professor
of petroleum and natural gas engineering
at West Virginia University and founder
of Intelligent Solutions Inc., a consulting
company with services, training and
products focused on artificial intelligence and data mining in the upstream oil
and gas industry. Mohaghegh holds a B.S.
and an M.S. in natural gas engineering
from Texas A&I University, and a Ph.D.
in petroleum and natural gas engineering
from Pennsylvania State University.

DAVID
MORENO

David Moreno is a reservoir engineer
at California Resources Corporation in
Bakersfield, Ca. He holds a B.S. in mechanical engineering from the Metropolitan University in Venezuela and an
M.S. in petroleum and natural gas engineering from West Virginia University.

164 THE AMERICAN OIL & GAS REPORTER

model, it was necessary to train neural networks, which were able to recognize patterns between the production histories of
the wells and the corresponding static
(reservoir properties, well completion information) and dynamic (operational information such as days of production per
month) data.
Once all possible inputs and outputs
were prepared, key performance indicator
analysis was performed to determine the
degree of influence each static or dynamic property had on production. Figure 3
displays the results of the KPI analysis,
while Table 1 lists the inputs for developing TDM.
Production data from all 145 wells beginning in 1986 and continuing through
December 2012 were used in TDM training. Reasonably good history match results
were achieved for both the entire field (Figure 4A) and individual wells (Figure 4B).
The green line represents TDM's production estimates and predictions, while the
dots represent actual data. Cumulative production for actual data and the TDM's predictions are shown by gray and green shaded areas, respectively. Future well production also was predicted for 2013 to 2015
(yellow shadow).
In order to validate the model, the production data for the final year were not
used in the training process. The trained
and history-matched model was used to
forecast production for the final year,
and the results were compared with actual available data. As shown for one of the
wells in Figure 5, the validated model is
capable of forecasting production.
Another key capability of the TDM
process is performing sensitivity analysis.
Reservoir and well design properties are uncertain, and any change in any property can
have an effect on production predictions.
Using sensitivity analysis, the effect of uncertainty in these parameters on production
forecasting was investigated using Monte
Carlo simulation for uncertainty/sensitivity analysis. The advantage of TDM was fast
computation time, which allowed performing up to thousands of production scenarios for Monte Carlo simulation.
The sensitivity of the production forecast to pay thickness values for one well
is shown in Figure 6. The yellow shaded
area is the range for the production forecast. The Gaussian distribution was considered for pay thickness with a mean value of 256 feet and a standard deviation of

Coming In February
Special Report:
Unconventional Resources Science,
Part II



American Oil and Gas Reporter - January 2015

Table of Contents for the Digital Edition of American Oil and Gas Reporter - January 2015

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