Abstract

Reserve estimation is a subject of continuous importance in the petroleum industry; controlling field development related decisions and providing valuation of corporations. Tight formations are usually completed with multistage hydraulic fractures and horizontal wellbores. However, these completion scheme results in heterogeneous fracture lengths and spacing. Consequently, some counterparts of the reservoir would experience boundary-dominated flow, while others are still experiencing an infinite-acting linear flow which creates a composite flow regime dubbed as complex fracture depletion (CFD). It is worth noting that the CFD flow regime might be preceded by a linear flow depending on the fracture complexity. We are proposing a unified model that integrates the flow regime analysis and the well performance analysis. Our model utilizes the derivative of the cumulative production with respect to the square root of produced time where the linear flow exhibits a horizontal line and the CFD exhibits an exponential straight line. Therefore, the onset of the CFD becomes the only variable for the regression analysis. Another consequence of utilizing an exponential fit of the flow derivative is a continuous reduction in the Arps’ “b” exponent from a “b” value of two during CFD. We also validated our model estimations to the estimations of Arap’s and stretch exponential production decline (SEPD) with recent production data from the Bone Spring formation, New Mexico, and major shale/tight reservoirs.

References

1.
Hughes
,
J. D.
,
2013
, “
Energy: A Reality Check on the Shale Revolution
,”
Nature
,
494
(
7437
), p.
307
. 10.1038/494307a
2.
LeFever
,
J. A.
, and
Helms
,
L. D.
,
2006
,
Bakken Formation Reserve Estimates
,
North Dakota Geological Survey
,
Bismarck, NC
.
3.
Clark
,
A. J.
,
2009
, “
Determination of Recovery Factor in the Bakken Formation, Mountrail County, ND
,”
SPE Annual Technical Conference and Exhibition
,
New Orleans, LA
,
October
,
Society of Petroleum Engineers
. https://doi.org/10.2118/133719-STU
4.
Bustin
,
R. M.
,
Bustin
,
A. M.
,
Cui
,
A.
,
Ross
,
D.
, and
Pathi
,
V. M.
,
2008
, “
Impact of Shale Properties on Pore Structure and Storage Characteristics
,”
SPE Shale Gas Production Conference
,
Fort Worth, TX
,
November
,
Society of Petroleum Engineers
.https://doi.org/10.2118/119892-MS
5.
Passey
,
Q. R.
,
Bohacs
,
K.
,
Esch
,
W. L.
,
Klimentidis
,
R.
, and
Sinha
,
S.
,
2010
, “
From Oil-Prone Source Rock to Gas-Producing Shale Reservoir-Geologic and Petrophysical Characterization of Unconventional Shale Gas Reservoirs
,”
International Oil and Gas Conference and Exhibition in China
,
Beijing, China
,
June
,
Society of Petroleum Engineers
. https://doi.org/10.2118/131350-MS
6.
Mehana
,
M.
, and
El-monier
,
I.
,
2016
, “
Shale Characteristics Impact on Nuclear Magnetic Resonance (NMR) Fluid Typing Methods and Correlations
,”
Petroleum
,
2
(
2
), pp.
138
147
. 10.1016/j.petlm.2016.02.002
7.
Santos
,
J. E.
,
Mehana
,
M.
,
Wu
,
H.
,
Prodanovic
,
M.
,
Kang
,
Q.
,
Lubbers
,
N.
,
Viswanathan
,
H.
, and
Pyrcz
,
M. J.
,
2020
, “
Modeling Nanoconfinement Effects Using Active Learning
,”
J. Phys. Chem. C
,
124
(
40
), pp.
22200
22211
. 10.1021/acs.jpcc.0c07427
8.
Mehana
,
M.
,
Callard
,
J.
,
Mansi
,
M.
, and
Gong
,
Y.
2019
, “
Integrating Production Analysis With Monte Carlo Simulation for Estimated Ultimate Recovery Eur Prediction
,”
SPE Eastern Regional Meeting
,
Charleston, VA
,
October
,
Society of Petroleum Engineers
https://doi.org/10.2118/196603-MS.
9.
Total Primary Energy Supply (TPES) by Source, World 1990–2017
, https://www.iea.org/data-and-statistics, Accessed January 14, 2020.
10.
Ayeni
,
B. J.
,
1989
, “
Parameter Estimation for Hyperbolic Decline Curve
,”
ASME. J. Energy Resour. Technol.
,
111
(
4
), pp.
279
283
. https://doi.org/10.1115/1.3231437
11.
Arps
,
J. J.
,
1945
, “
Analysis of Decline Curves
,”
Trans. AIME
,
160
(
1
), pp.
228
247
. 10.2118/945228-G
12.
Valko
,
P. P.
,
2009
, “
Assigning Value to Stimulation in the Barnett Shale: A Simultaneous Analysis of 7000 Plus Production Hystories and Well Completion Records
,”
SPE Hydraulic Fracturing Technology Conference
,
The Woodlands, TX
,
January
,
Society of Petroleum Engineers
. https://doi.org/10.2118/119369-MS
13.
Ilk
,
D.
,
Currie
,
S. M.
,
Symmons
,
D.
,
Rushing
,
J. A.
, and
Blasingame
,
T. A.
,
2010
, “
Hybrid Rate-Decline Models for the Analysis of Production Performance in Unconventional Reservoirs
,”
SPE Annual Technical Conference and Exhibition
,
Florence, Italy
,
September
,
Society of Petroleum Engineers
. https://doi.org/10.2118/135616-MS
14.
Duong
,
A. N.
,
2010
, “
An Unconventional Rate Decline Approach for Tight and Fracture-Dominated Gas Wells
,”
Canadian Unconventional Resources and International Petroleum Conference
,
Calgary, Alberta, Canada
,
October
,
Society of Petroleum Engineers
. https://doi.org/10.2118/137748-MS
15.
Joshi
,
K.
, and
Lee
,
W. J.
,
2013
, “
Comparison of Various Deterministic Forecasting Techniques in Shale Gas Reservoirs
,”
SPE Hydraulic Fracturing Technology Conference
,
The Woodlands, TX
,
February
,
Society of Petroleum Engineers
. https://doi.org/10.2118/163870-MS
16.
Statton
,
J. C.
,
2012
, “
Application of the Stretched Exponential Production Decline Model to Forecast Production in Shale Gas Reservoirs
,”
PhD thesis
,
Texas A & M University
,
College Station, TX
.
17.
Ilk
,
D.
,
Rushing
,
J. A.
, and
Blasingame
,
T. A.
,
2011
, “
Integration of Production Analysis and Rate-Time Analysis Via Parametric Correlations—Theoretical Considerations and Practical Applications
,”
SPE Hydraulic Fracturing Technology Conference
,
The Woodlands, TX
,
January
,
Society of Petroleum Engineers
. https://doi.org/10.2118/140556-MS
18.
Clark
,
A. J.
,
Lake
,
L. W.
, and
Patzek
,
T. W.
,
2011
, “
Production Forecasting With Logistic Growth Models
,”
SPE Annual Technical Conference and Exhibition
,
Denver, CO
,
October
,
Society of Petroleum Engineers
. https://doi.org/10.2118/144790-MS
19.
Qin
,
J.
,
Cheng
,
S.
,
He
,
Y.
,
Wang
,
Y.
,
Feng
,
D.
,
Yang
,
Z.
,
Li
,
D.
, and
Yu
,
H.
,
2019
, “
Decline Curve Analysis of Fractured Horizontal Wells Through Segmented Fracture Model
,”
ASME J. Energy. Resour. Technol.
,
141
(
1
), p.
012903
. 10.1115/1.4040533
20.
Cheng
,
Y.
,
Lee
,
W. J.
, and
McVay
,
D. A.
,
2008
, “
Quantification of Uncertainty in Reserve Estimation From Decline Curve Analysis of Production Data for Unconventional Reservoirs
,”
ASME J. Energy. Resour. Technol.
,
130
(
4
), p.
043201
. 10.1115/1.3000096
21.
Mehana
,
M.
, and
Callard
,
J.
,
2018
, “
Reserve Estimation With Unified Production Analysis
,”
In Unconventional Resources Technology Conference
,
Houston, TX
,
July 23–25
,
Society of Exploration Geophysicists, American Association of Petroleum
, pp.
691
696
.
22.
Jochen
,
V.
, and
Spivey
,
J.
,
1996
, “
Probabilistic Reserves Estimation Using Decline Curve Analysis With the Bootstrap Method
,”
SPE Annual Technical Conference and Exhibition
,
Denver, CO
,
October
,
Society of Petroleum Engineers
. https://doi.org/10.2118/36633-MS
23.
Cheng
,
Y.
,
Wang
,
Y.
,
McVay
,
D.
, and
Lee
,
W. J.
,
2010
, “
Practical Application of a Probabilistic Approach to Estimate Reserves Using Production Decline Data
,”
SPE Economics Manage.
,
2
(
1
), pp.
19
31
. 10.2118/95974-PA
24.
Gong
,
X.
,
Gonzalez
,
R.
,
McVay
,
D. A.
, and
Hart
,
J. D.
,
2014
, “
Bayesian Probabilistic Decline-Curve Analysis Reliably Quantifies Uncertainty in Shale-Well-Production Forecasts
,”
SPE. J.
,
19
(
6
), pp.
1
47
. 10.2118/147588-PA
25.
Paryani
,
M.
,
Awoleke
,
O. O.
,
Ahmadi
,
M.
,
Hanks
,
C.
, and
Barry
,
R.
,
2017
, “
Approximate Bayesian Computation for Probabilistic Decline-Curve Analysis in Unconventional Reservoirs
,”
SPE. Reservoir. Eval. Eng.
,
20
(
2
), pp.
478
485
. 10.2118/183650-PA
26.
Joshi
,
K. G.
,
Awoleke
,
O. O.
, and
Mohabbat
,
A.
,
2018
, “
Uncertainty Quantification of Gas Production in the Barnett Shale Using Time Series Analysis
,”
SPE Western Regional Meeting
,
Garden Grove, CA
,
April
,
Society of Petroleum Engineers
https://doi.org/10.2118/190124-MS.
27.
Mehana
,
M.
,
Callard
,
J.
,
Kang
,
Q.
, and
Viswanathan
,
H.
,
2020
, “
Monte Carlo Simulation and Production Analysis for Ultimate Recovery Estimation of Shale Wells
,”
J. Nat. Gas Sci. Eng.
,
83
, p.
103584
. 10.1016/j.jngse.2020.103584
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