SPE DISTINGUISHED LECTURER SERIES is funded principally SPE FOUNDATION

SPE DISTINGUISHED LECTURER SERIES is funded principally through a grant of the SPE FOUNDATION The Society gratefully acknowledges those companies tha...
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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