– Artificial Neural Network for Diagnosis & Mitigation of Water Production –
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ABSTRACT
From the inception of the oil and gas industry, water production has always been an aching problem for various operators.
Throughout the productive life of a field, oil production is often accompanied with some amount of water production, which in most cases, is so significant and unwanted.
Because of the great operating, environmental and economic challenges associated with excess water production, operators are in search for different methods and tools that could be used to identify the sources of water and prevent or mitigate early water breakthrough in producing oil and gas wells.
In this study, artificial neural network (ANN) models were developed and used as reservoir management tools to proffer solution to mitigate excess water production problems.
Two cases were considered to build and train the network models. In case one, three neural network models were developed and optimized by training with data from only one producing well.
The built network models are called CASNNET1, CASNNET2, and CASNNET3, respectively. In case two, a neural network model called CASNNET4 was developed by using combined data from two producing wells for training, validation and testing.
The neural network models in both cases were developed to predict well water cut with an appreciable degree of accuracy.
TABLE OF CONTENT
ABSTRACT…………………. ii
DEDICATION…………………iii
ACKNOWLEDGEMENT…………… iv
NOMENCLATURE ………………… v
LIST OF FIGURES ……………….. ix
LIST OF TABLES…………………. xiii
LIST OF APPENDICES………………. xv
CHAPTER ONE
Introduction…………… 1
1.1 Statement of Problem………….. 1
1.2 Objectives of Study ………………. 2
1.3 Expected Outcomes…………………… 2
1.4 Scope of Work……………….. 3
CHAPTER TWO
Literature Review…………….. 4
2.1 Diagnosis of Water Production Mechanisms……………….. 4
2.2 Mitigation of Excess Water Production …………….. 20
2.3 Application of Artificial Neural Network (ANN)……………. 25
2.3.1 ANN Application in Medicine…………….. 25
2.3.2 ANN Application in Oil & Gas Industry ……….. 27
CHAPTER THREE
Methodology…………….. 29
3.1 Study Methodology and Workflow………… 29
3.2 Reservoir Simulation…………. 29
3.2.1 Reservoir XY Model Description ……….. 29
3.2.2 SENSOR Simulator …………. 32
3.2.3 Simulation Runs……….. 33
3.3 Concept of Artificial Neural Networks (ANNs) ……….. 33
3.3.1 Classification of Neural Networks………….. 34
3.3.2 Methodology for Developing Neural Networks……… 35
3.3.3 Neural Network Models……………….. 39
3.3.4 Testing the Neural Network Models on New Data………… 42
CHAPTER FOUR
Results & Discussion …………………… 44
4.1 Reservoir Simulation…………………. 44
4.1.1 Maps of the Reservoir Model …………….. 44
4.1.2 Production Parameters Plots…………… 49
4.2 Neural Network Models……………….. 51
4.2.1 Case One – Results of Training Network with One Well………….. 51
4.2.2 Case 2 – Results of Training Network with Two Wells……….. 59
4.2.3 Results of Testing Neural Network Models on New Wells………… 63
4.3 Observation & Analysis………………. 70
4.3.1 Sensitivity Analysis ………… 70
CHAPTER FIVE
Conclusions & Recommendations…………….. 79
5.1 Conclusions…………………… 79
5.2 Recommendations…………… 80
REFERENCES ………. 82
STATEMENT OF PROBLEM
During the production of oil and gas from petroleum reservoirs, water production can come from an adjoining aquifer or from water injection wells in a waterflooding process.
When there is excess water production, there is a cost associated with operating both subsurface and surface production facilities, scale and corrosion problems.
Besides, the recovery factor is decreased due to the bypass of oil by the displacement water front. These factors pose significant financial and environmental challenges for the petroleum industry.
Gasbarri et al., 2008, classified problems associated with excessive water production into two broad categories:
- Problems associated with the reservoir, e.g., water coning, channelization (premature breakthrough of water in producing wells through channels of high permeability leaving fluid back in zones of low permeability), and movement of the oil-water contact.
- Problems associated with near wellbore flow, e.g., flow behind casing. Very importantly, to accurately predict and mitigate the amount of produced water, the sources or mechanisms of the water production must be properly identified.
REFERENCES
A. Saeedi et al., 2006, Using Neural Networks for Candidate Selection & Well Performance Prediction in Water-Shutoff Treatments Using Polymer Gels, SPE 101028. A Paper Presented at the Asia Pacific Oil & Gas Conference & Exhibition held in Adelaide, Australia, 11-13September.
C. M. Reyes et al., 2010, A Reliability-Based Systemic Method for Water Production Analysis, Diagnosis and Solution Design, SPE138935. A Paper Presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru, December 1-3.
Coats Engineering Inc., 2011, SENSOR Compositional and Black Oil Simulation Software Manual, April 1, 2011.
D. Perez et al., 2001, Applications of Polymer Gel for Establishing Zonal Isolations & Water Shutoff in Carbonate Formations, SPE 73196. A Paper Presented at the 1997 SPE/IADC Drilling Conference, Amsterdam, March 4 – 6; revised for publication in 1997 as SPE 37622 and in 2001.
Dr. N. Ganesan et al., 2010, Application of Neural networks in Diagnosing Cancer Disease using Demographic Data, International Journal of Computer Applications (0975-8887), Volume 1 – No. 26.
Gasbarri et al., 2008, Water Production Diagnosis using Transient Test with Multiphase Flowmeter, SPE-117236. A Paper presented at the SPE Eastern Regional/AAPG Eastern Section Joint Meeting held in Pittsburgh, Pennsylvania, USA, October 11-15.
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