SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the
SPE FOUNDATION The Society gratefully acknowledges those companies that support the program by allowing their professionals to participate as Lecturers. And special thanks to The American Institute of Mining, Metallurgical, and Petroleum Engineers (AIME) for their contribution to the program. Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
SPE Distinguished Lecture 2007-2008
Smart Completions, Smart Wells and Now Smart Fields; Challenges & Potential Solutions Shahab D. Mohaghegh, Ph.D. g University y& West Virginia Intelligent Solutions, Inc.
2
Smart Oil Field Technology
Smart Completion:
Remotely monitor & control downhole fluid production or injection. Downhole control to adjust flow distributions along the wellbore to correct undesirable fl id ffrontt movement. fluid t
3 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Smart Oil Field Technology
Smart Well: Using permanent downhole gauges for continuous monitoring of pressure, flow rates, … and automatic fl flow controls. t l Capability of automatic interaction using extensive downhole communication. 4
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Smart Oil Field
The Missing link
5 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Characteristics of Smart Fields
Availability of high frequency data.
The Missing link
Making reservoir managementt decisions based on real time data from the field.
Possibility of intervention, control and management from a distance. 6
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Characteristics of Smart Fields
Availability of high frequency data.
The Missing link
Making reservoir managementt decisions based on real time data from the field.
Possibility of intervention, control and management from a distance. 7
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Characteristics of Smart Fields
Making reservoir management decisions based on real-time data from the field. Considerations:
Reservoir management tools. Uncertainties associated with the geological model. Predicting the consequences of the decision. Real-time optimization.
8 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Hardware / Software
Intelligence requires a combination of hardware and software. We have made strong g advances in hardware. Software development is lagging. Intelligent Systems will play a pivotal role: Artificial Neural Networks Fuzzy Set Theory Genetic Optimization 9
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Hardware / Software
Surrogate Reservoir Models (SRM) are developed to address the software need of smart fields. SRM are reservoir management tools for smart fields:
Real-time full field reservoir simulation & modeling Predictive modeling Uncertainty analysis Real-time R l ti optimization ti i ti 10
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Removing The Bottle-Neck Real-Time, High Frequency Data Stream
Full Field Flow Models for Reservoir Simulation & Modeling. One of the major tools for integrated Reservoir Management
Ti Scale: Time S l
Ti Scale: Time S l
Seconds, Minutes, Hours
Days, Months, ….
How can the bottle-neck be removed? Perform analysis at the same time scale as the High Frequency Data Streams; in seconds, or better yet, in REAL TIME REAL-TIME 11 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
SURROGATE RESERVOIR MODEL Definition
Surrogate Reservoir Models are replicas of the numerical simulation models (full field flow models) that run in real-time real time.
REPLICA.
A copy or reproduction d ti off a workk off art, t especially i ll one made by the original artist. A copy or reproduction reproduction, especially one on a scale smaller than the original. Something g closely y resembling g another. 12
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Characteristics of SRM
SRMs are not
response surfaces. statistical representations of simulation models models.
SRMs are
engineering tools honor the physics of the problem in hand. adhere to the definition of “System System Theory” Theory . INPUT
SYSTEM
OUTPUT
13 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Case Study
Lets see an example of a Surrogate Reservoir Model in action. This case study demonstrates development of a surrogate reservoir model (SRM) that will run in real-time real time in order to accomplish the objectives of the project.
14 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Background
A giant oil field in the Middle East East. Complex carbonate formation. 165 horizontal wells wells. Total field production capped at 250,000 BOPD. Each well is capped at 1,500 BOPD. Water injection for pressure maintenance.
15 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Background
Management Concerns:
Water production is becoming a problem. Cap well production to avoid bypass oil oil. Uncertainties associated with models.
Technical Team’s Team s Concerns:
May be able to produce more oil from some wells ((which ones? How much increase?)) without significant increase in water cut. Increasing well rate may actually help recovery. 16
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Objective
Increase oil production from the field by identifying wells that:
will not suffer from high water cut. will not leave bypassed oil behind.
17 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Objective
Accomplishing this objective requires:
Exhaustive search of the solution space, examining all possible production scenarios, while considering uncertainties associated with the geological model model. Hundreds of thousands of simulation runs; thus development of a Surrogate Reservoir Model (SRM) based on the Full Field Model (FFM) became a requirement.
18 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Flow Model Characteristics
Full Field Flow Model Characteristics:
Underlying Complex Geological Model. Industry Standard Commercial Reservoir Si l t Simulator 165 Horizontal Wells. Approximately 1,000,000 grid blocks. Single Run = 10 Hours on 12 CPUs.
19 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Very Complex Geology Naturally Fractured Carbonate Reservoir.
Reservoirs represented in th Flow the Fl M Model. d l 20 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Steps Involved in SRM Development
Identify Clear Objectives Design SRM’s input and output Generate Data Build SRM Validate Analyze Results & Conclusions
21 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
SRM’s Objective
Accurately Reproduce the following for the next 25 to 40 years.
Cumulative Oil Production Cumulative Water Production Instantaneous Water Cut
22 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
SRM’s Input & Output
OUTPUT was identified by the Objective
Cumulative Oil Production Cumulative Water Production Instantaneous Water Cut
INPUT must be designed in a way to capture the complexity of the reservoir.
Well based SRM Well-based Well-based SRM grid Curse of dimensionality 23
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality
Complexity of a system increases with its dimensionality. Tracking system behavior becomes increasingly difficult as the number of dimensions increases. Systems do not behave in the same manner in all dimensions dimensions.
Some are more detrimental than others.
24 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality
Sources of dimensionality:
STATIC: Representation of reservoir properties associated with each well. DYNAMIC: Simulation runs to demonstrate well productivity.
25 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Well-Based Surrogate Reservoir Model
Surrogate Model Elemental Volume. 1
2
3
4
5
6
7
8
1 2 3 4 5 The elemental volume includes 40 SRM blocks. 26 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality, Static
Potential list of parameters that can be collected on a “per-well” p basis. Parameters Used on a per well basis Latitude
Longitude
Deviation
Azimuth
Horizontal Well Length
Productivity Index
Distance to Free Water Level
Water Cut @ Reference Point
Flowing BHP @ Reference Point
Oil Prod. Rate @ Reference Point
Cum. Oil Prod. @ Reference Point
Cum. Water Prod. @ Reference Point
Distance to Nearest Producer
Distance to Nearest Injector j
Distance to Major Fault
Distance to Minor Fault 16 Parameters
27 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality, Static
Potential list of parameters that can be collected on a “per-SRM block” basis. Parameters Used on a per segment basis Mid Depth
Thickness
Relative Rock Ttype Initial Water Saturations
Porosity Stylolite Intensity
Horizontal Permeability
Vertical Permeability
Sw @ Reference Point
So @ Reference Point
Capillary Pressure/Saturation Function
Pressure @ Reference Point 12 Parameters
28 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality, Static
Total number of parameters that need representation during the modeling process: ¾
12 parameters t x 40 grid id block/well bl k/ ll = 480
¾
16 parameter per well
¾
Total of 496 parameter per well
Building a model with 496 parameters per well is not realistic, THE CURSE OF DIMENSIONALITY
Dimensionality Reduction becomes a vital task. 29
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality, Dynamic
Wellll productivity W d ti it iis id identified tifi d th through h following simulation runs:
All wells ll producing d i att 1500, 1500 2500 2500, 3500 3500, & 4500 bpd (nominal rates)
No cap on field productivity (4 simulation runs) Cap the field productivity (4 simulation runs)
Need to understand reservoir’s response to changes in imposed constraints. 30 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Curse of Dimensionality, Dynamic
Well productivity through following simulation i l ti runs:
Step up the rates for all wells
No cap on field productivity (1 simulation runs) Cap the field productivity (1 simulation runs)
Need to understand reservoir’s response to changes in imposed constraints. 31 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Data Generation
Total of 10 simulation runs were made to generate t the th required i d output t t for f the th SRM development (training, calibration & validation) Using Fuzzy Pattern Recognition technology input to the SRM was compiled compiled.
32 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Fuzzy Pattern Recognition
IIn order d to t address dd th the “C “Curse off Dimensionality” one must understand the behavior and contribution of each of the parameters to the process being modeled. Not a simple and straight forward task task. !!!
33 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Fuzzy Pattern Recognition
To address this issue, we use Fuzzy Pattern Recognition technology.
34 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Fuzzy Pattern Recognition
Parameter: Pressure @ Reference 35 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Fuzzy Pattern Recognition
36 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Key Performance Indicators
37 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Water Cutt % (Surroga ate Model)
Validation of the SRM
Water Cut % (Reservoir Simulator)
38 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Cumulative Oil Prroduction (Surrogate Mo odel)
Validation of the SRM
Cumulative Oil Production ((Reservoir Simulator))
39 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
40 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Validation of the SRM
41 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Validation of the SRM
42 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Validation of the SRM
43 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Validation of the SRM
44 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Using SRM for Analysis
Identify wells that benefit from a rate increase and those that would not. Address the uncertainties associated with the simulation model. Generate Type curves for each well well.
Design production strategy. Use as assisted history matching tool tool. To p perform the above analyses y millions of simulation runs were required. q Using the SRM all such analyses were performed quite quickly. 45
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Optimal Production Strategy
Well Ranked No. 1
IMPORTANT NOTE: This is NOT a Response p Surface – SRM was run hundreds of times to generate these figures. 46 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Optimal Production Strategy
Well ll Ranked k d No. 100 IMPORTANT NOTE: This is NOT a Response Surface – SRM was run hundreds of times i to generate these h figures. fi 47 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Optimal Production Strategy
Wells were divided into 5 clusters clusters. Production in wells in cluster 1 can be increased significantly without substantial increase in water production. Cl Cluster 1
12 Wells
Cluster 2
Cluster 3
Cluster 4
Cluster 5
14 Wells
22 Wells
37 Wells
80 Wells
Best Performance 48 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis y of Uncertaintyy
Objective:
To address and analyze the uncertainties associated with the Full Field Model using g Monte Carlo simulation method.
49 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty
Motivation:
The Full Field Model is a reservoir simulator that is based on a g geologic g model. The geologic model is developed based on a set of measurements (logs, core analysis, seismic, …) and corresponding geological and geophysical interpretations.
50 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty
Motivation:
Therefore, like any other reservoir simulation and modeling g effort,, it includes certain obvious uncertainties. One of the outcomes of this project has been the identification of a small set of reservoir parameters that essentially control the production behavior in the horizontal wells in this field (KPIs) (KPIs).
51 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty Following are the steps involved:
Identify a set of key performance indicators that are most vulnerable to uncertainty. y Define probability distribution function for each of the performance indicators.
1.
2.
a. b. c c. d.
Uniform distribution Normal (Gaussian) distribution Triangular distribution Discrete distribution
52 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty Following are steps involved:
3.
4.
Run the neural network model hundreds or thousands of times using g the defined p probability y distribution functions for the identified reservoir parameters. Performing this analysis using the act al F actual Fullll Field Model is impractical impractical. Produce a probability distribution function for cumulative oil production and the water cut at different time and liquid rate cap.
53 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty Following are steps involved:
5.
Such results bounds to be much more reliable and therefore,, more acceptable p to the management or skeptics of the reservoir modeling studies.
54 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty
55 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty
56 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty Average g Sw @ Reference p point in Top p Layer II
Value in the model = 8% Lets use a minimum of 4% and a maximum of 15% with a triangular distribution
4
8
15
57 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty Average Capillary Pressure @ Reference point in Top Layer III
Value in the model = 79 psi Lets use a minimum of 60 psi and a maximum of 100 psi with a triangular distribution
60
80
00 100 58
Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Analysis of Uncertainty
PDF for HB001 Cumulative Oil and Cumulative Water production at the rate of 3,000 blpd cap after 20 years. Actual Models are available & can be demonstrated after the p presentation.
59 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Type Curves Type curves can be generated in seconds to address sensitivity of oil and water production to all involved p p parameters. Type curves can be generated for:
Individual wells Each cluster of wells Entire field
60 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Type Curves Cum. Oil Production as a function of Average Horizontal Permeability in one of the top layers.
61 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Type Curves Water Cut as a function of Average Horizontal Permeability in the well layers.
62 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Type Curves Water Cut as a function of Average Vertical Permeability in one of the top layers.
63 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Type Curves Water Cut as a function of Average Vertical Permeability in the Well layers.
64 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Results & Conclusions
Upon completion of the project management allowed production increase in six cluster one wells. After 8 months of successful production rest of the cluster one wells were also put on higher production. It has been more than 15 months since the results were implemented with success.
65 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
Results & Conclusions
A successful surrogate reservoir model was developed for a giant oil field in the Middle East. The surrogate model was able to accurately mimic the behavior of the actual full field flow model in real-time.
66 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008
CONCLUSIONS
Development of successful surrogate reservoir model is an important and essential step p toward development p of next g generation of reservoir management tools that would address the needs of smart fields.
67 Shahab D. Mohaghegh, Ph.D. – WVU & ISI
SPE Distinguished Lecture Series, 2007 - 2008