Abstract

Oil and gas are likely the most important sources for producing heat and energy in both domestic and industrial applications. Hydrocarbon reservoirs that contain these fuels are required to be characterized to exploit the maximum amount of their fluids. Well testing analysis is a valuable tool for the characterization of hydrocarbon reservoirs. Handling and analysis of long-term and noise-contaminated well testing signals using the traditional methods is a challenging task. Therefore, in this study, a novel paradigm that combines wavelet transform (WT) and recurrent neural networks (RNN) is proposed for analyzing the long-term well testing signals. The WT not only reduces the dimension of the pressure derivative (PD) signals during feature extraction but it efficiently removes noisy data. The RNN identifies reservoir type and its boundary condition from the extracted features by WT. Results confirmed that the five-level decomposition of the PD signals by the Bior 1.1 filter provides the best features for classification. A two-layer RNN model with nine hidden neurons correctly detects 3202 out of 3298 hydrocarbon reservoir systems. Performance of the proposed approach is checked using smooth, noisy, and real field well testing signals. Moreover, a comparison is done among predictive accuracy of WT-RNN, traditional RNN, conventional multilayer perceptron (MLP) neural networks, and couple WT-MLP approaches. The results confirm that the coupled WT-RNN paradigm is superior to the other considered smart machines.

References

1.
Ahmed
,
T.
,
2018
,
Reservoir Engineering Handbook
, 4th ed.,
Gulf Professional Publishing
,
Oxford
.
2.
Bahadori
,
A.
,
2018
,
Fundamentals of Enhanced oil and Gas Recovery From Conventional and Unconventional Reservoirs
, 1st ed.,
Gulf Professional Publishing
,
Oxford
.
3.
Yang
,
R.
,
Jiang
,
R.
,
Patil
,
S.
,
Liu
,
S.
,
Gao
,
Y.
,
Chen
,
H.
, and
Sun
,
Z.
,
2019
, “
Comprehensive Well Test Interpretation Method, Process, and Multiple Solutions Analysis for Complicated Carbonate Reservoirs
,”
ASME J. Energy Resour. Technol.
,
41
(
12
), p.
122906
. 10.1115/1.4044801
4.
Taleghani
,
A. D.
, and
Ahmadi
,
M.
,
2020
, “
Thermoporoelastic Analysis of Artificially Fractured Geothermal Reservoirs: A Multiphysics Problem
,”
ASME J. Energy Resour. Technol.
,
142
(
8
), p.
081302
. 10.1115/1.4045925
5.
Ren
,
J.
,
Gao
,
Y.
,
Zheng
,
Q.
, and
Wang
,
D.
,
2020
, “
Pressure Transient Analysis for a Finite-Conductivity Fractured Vertical Well Near a Leaky Fault in Anisotropic Linear Composite Reservoirs
,”
ASME J. Energy Resour. Technol.
,
142
(
7
), p.
073002
. 10.1115/1.4046456
6.
Zhang
,
Q.
,
Wang
,
D.
,
Zeng
,
F.
,
Guo
,
Z.
, and
Wei
,
N.
,
2020
, “
Pressure Transient Behaviors of Vertical Fractured Wells With Asymmetric Fracture Patterns
,”
ASME J. Energy Resour. Technol.
,
142
(
4
), p.
043001
. 10.1115/1.4045226
7.
Ouyang
,
L. B.
, and
Kikani
,
J.
,
2005
, “
New Approaches for Permanent Downhole Gauge (PDG) Data Processing
,”
Petrol. Sci. Technol.
,
23
(
9–10
), pp.
1247
1263
.
8.
Vaferi
,
B.
,
Eslamloueyan
,
R.
, and
Ghaffarian
,
N.
,
2016
, “
Hydrocarbon Reservoir Model Detection From Pressure Transient Data Using Coupled Artificial Neural Network-Wavelet Transform Approach
,”
Appl. Soft Comput.
,
47
, pp.
63
75
. 10.1016/j.asoc.2016.05.052
9.
Udegbe
,
E.
,
Morgan
,
E.
, and
Srinivasan
,
S.
,
2019
, “
Big-Data Analytics for Production-Data Classification Using Feature Detection: Application to Restimulation-Candidate Selection
,”
SPE Reserv. Eval. Eng.
,
22
(
2
), pp.
1
22
.
10.
Vaferi
,
B.
,
Eslamloueyan
,
R.
, and
Ayatollahi
,
S.
,
2011
, “
Automatic Recognition of Oil Reservoir Models From Well Testing Data by Using Multi-Layer Perceptron Networks
,”
J. Petrol. Sci. Eng.
,
77
(
3–4
), pp.
254
262
. 10.1016/j.petrol.2011.03.002
11.
Ghaffarian
,
N.
,
Eslamloueyan
,
R.
, and
Vaferi
,
B.
,
2014
, “
Model Identification for Gas Condensate Reservoirs by Using ANN Method Based on Well Test Data
,”
J. Petrol. Sci. Eng.
,
123
, pp.
20
29
. 10.1016/j.petrol.2014.07.037
12.
Allain
,
O. F.
, and
Horne
,
R. N.
,
1990
, “
Use of Artificial Intelligence in Well-Test Interpretation
,”
J. Petrol. Technol.
,
42
(
03
), pp.
342
349
. 10.2118/18160-PA
13.
Al-Kaabi
,
A. U.
, and
Lee
,
W. J.
,
1993
, “
Using Artificial Neural Networks to Identify the Well Test Interpretation Model
,”
SPE Format. Eval.
,
8
(
03
), pp.
233
240
. 10.2118/20332-PA
14.
Athichanagorn
,
S.
, and
Horne
,
R. N.
,
1995
, “
Automatic Parameter Estimation From Well Test Data Using Artificial Neural Network
,”
SPE Annual Technical Conference and Exhibition
,
Dallas, TX
,
Oct. 22–25
, pp.
249
262
.
15.
Vaferi
,
B.
,
Eslamloueyan
,
R.
, and
Ayatollahi
,
S.
,
2015
, “
Application of Recurrent Networks to Classification of Oil Reservoir Models in Well-Testing Analysis
,”
Energy Sources, Part A
,
37
(
2
), pp.
174
180
. 10.1080/15567036.2011.582610
16.
Tian
,
C.
, and
Horne
,
R. N.
,
2017
, “
Recurrent Neural Networks for Permanent Downhole Gauge Data Analysis
,”
SPE Annual Technical Conference and Exhibition
,
San Antonio, TX
,
Oct. 9–11
, pp.
1
12
.
17.
Tian
,
C.
, and
Horne
,
R. N.
,
2019
, “
Applying Machine Learning Techniques to Interpret Flow Rate, Pressure and Temperature Data From Permanent Downhole Gauges
,”
SPE Reserv. Eval. Eng.
,
22
(
2
), pp.
1
16
.
18.
Khan
,
K. A.
,
Shanir
,
P. P.
,
Khan
,
Y. U.
, and
Farooq
,
O.
,
2020
, “
A Hybrid Local Binary Pattern and Wavelets Based Approach for EEG Classification for Diagnosing Epilepsy
,”
Expert Syst. Appl.
,
140
, pp.
112895
. 10.1016/j.eswa.2019.112895
19.
Xue
,
Y. J.
,
Cao
,
J.
,
Zhang
,
G. L.
,
Cheng
,
G. H.
, and
Chen
,
H.
,
2018
, “
Application of Synchrosqueezed Wavelet Transforms to Estimate the Reservoir Fluid Mobility
,”
Geophys. Prospect.
,
66
(
7
), pp.
1358
1371
. 10.1111/1365-2478.12622
20.
Athichanagorn
,
S.
,
Horne
,
R. N.
, and
Kikani
,
J.
,
2002
, “
Processing and Interpretation of Long-Term Data Acquired From Permanent Pressure Gauges
,”
SPE Reserv. Eval. Eng.
,
5
(
05
), pp.
384
391
. 10.2118/80287-PA
21.
Vaferi
,
B.
, and
Eslamloueyan
,
R.
,
2015
, “
Hydrocarbon Reservoirs Characterization by Co-interpretation of Pressure and Flow Rate Data of the Multi-Rate Well Testing
,”
J. Petrol. Sci. Eng.
,
135
, pp.
59
72
. 10.1016/j.petrol.2015.08.016
22.
Vaferi
,
B.
,
Salimi
,
V.
,
Baniani
,
D. D.
,
Jahanmiri
,
A.
, and
Khedri
,
S.
,
2012
, “
Prediction of Transient Pressure Response in the Petroleum Reservoirs Using Orthogonal Collocation
,”
J. Petrol. Sci. Eng.
,
98–99
, pp.
156
163
. 10.1016/j.petrol.2012.04.023
23.
Moosavi
,
S. R.
,
Vaferi
,
B.
, and
Wood
,
D. A.
,
2018
, “
Applying Orthogonal Collocation for Rapid and Reliable Solutions of Transient Flow in Naturally Fractured Reservoirs
,”
J. Petrol. Sci. Eng.
,
162
, pp.
166
179
. 10.1016/j.petrol.2017.12.039
24.
Nategh
,
M.
,
Vaferi
,
B.
, and
Riazi
,
M.
,
2019
, “
Orthogonal Collocation Method for Solving the Diffusivity Equation: Application on Dual Porosity Reservoirs With Constant Pressure Outer Boundary
,”
ASME J. Energy Resour. Technol.
,
141
(
4
), p.
042001
. 10.1115/1.4041842
25.
Bourdet
,
D.
,
Ayoub
,
J.
, and
Pirard
,
Y.
,
1989
, “
Use of Pressure Derivative in Well Test Interpretation
,”
SPE Format. Eval.
,
4
(
02
), pp.
293
302
. 10.2118/12777-PA
26.
Daubechies
,
I.
,
1990
, “
The Wavelet Transform, Time-Frequency Localization and Signal Analysis
,”
IEEE Trans. Inf. Theory
,
36
(
5
), pp.
961
1005
. 10.1109/18.57199
27.
Suzuki
,
K.
,
2013
,
Artificial Neural Networks: Architectures and Applications
, 1st ed.,
IntechOpen
,
Rijeka
.
28.
Abdelgawad
,
K.
,
Elkatatny
,
S.
,
Moussa
,
T.
,
Mahmoud
,
M.
, and
Patil
,
S.
,
2019
, “
Real-Time Determination of Rheological Properties of Spud Drilling Fluids Using a Hybrid Artificial Intelligence Technique
,”
ASME J. Energy Resour. Technol.
,
141
(
3
), p.
032908
. 10.1115/1.4042233
29.
Elman
,
J. L.
,
1990
, “
Finding Structure in Time
,”
Cog. Sci.
,
14
(
2
), pp.
179
211
. 10.1207/s15516709cog1402_1
30.
Yu
,
B.
,
Kim
,
D.
,
Cho
,
H.
, and
Mago
,
P.
,
2020
, “
A Nonlinear Autoregressive With Exogenous Inputs Artificial Neural Network Model for Building Thermal Load Prediction
,”
ASME J. Energy Resour. Technol.
,
142
(
5
), p.
050902
. 10.1115/1.4045543
31.
Mandic
,
D. P.
, and
Chambers
,
J.
,
2001
,
Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability
, 1st ed.,
Wiley
,
Chichester
.
32.
Majdi
,
A.
, and
Beiki
,
M.
,
2010
, “
Evolving Neural Network Using a Genetic Algorithm for Predicting the Deformation Modulus of Rock Masses
,”
Int. J. Rock Mech. Min.
,
47
(
2
), pp.
246
253
. 10.1016/j.ijrmms.2009.09.011
33.
Du
,
K. L.
, and
Swamy
,
M. N. S.
,
2006
,
Neural Networks in a Softcomputing Framework
, 1st ed.,
Springer
,
London
.
34.
Galdi
,
V.
,
Pierro
,
V.
, and
Pinto
,
I. M.
,
1998
, “
Evaluation of Stochastic-Resonance-Based Detectors of Weak Harmonic Signals in Additive White Gaussian Noise
,”
Phys. Rev. E
,
57
(
6
), pp.
6470
6479
. 10.1103/PhysRevE.57.6470
35.
Horne
,
R. N.
,
1995
,
Modern Well Test Analysis-A Computer Aided Approach
, 1st ed.,
Petroway
,
CA
.
You do not currently have access to this content.