razi gaskari - tecnica integral de datos de produccion

8/13/2019 Razi Gaskari - Tecnica Integral de Datos de Produccion

1/28

An Integrated Technique forProduction Data Analysis with

Application to Mature Fields.

Razi Gaskari

Shahab D. Mohaghegh, Jalal Jalali

West Virginian University

SPE 100562

SPE Gas Technology Symposium, Calgary, Alberta, Canada - May 2006.


2/28

Introduction

The most common data that engineers cancount on, specially in the case of mature

fields is PRODUCTION RATE DATA.

SPE 100562

Introduction Objective Methodology Result and Discussion Conclusion


3/28

Introduction

History of production data analysis:

SPE 100562


1945 Arps et al. Liquid systems, Rate-time type curves, PSSconditions, parameter b, no physical bases.

1980 Fetkovich: Liquid systems, Constant pressure, Early time,

Transient analytical solution + Arps.

1985 Carter: Liquid and gas systems, constant BHP, parameter ()

1987 Wattenbarger: Gas systems, modified Fetkovich type curves,

normalize time, long term boundary dominated (PSS).

1993 Palacio, Blasingame: Liquid and gas systems , Radial flow,

dimensionless variables, equivalent constant rate liquid data,

derivative methods. 1995 Cox: Gas flow, tight and hydraulically fractured reservoir.

1999 Agarwal et al.: Liquid and Gas Radial systems, Fractured wells,

Finite and Infinite conductivity


4/28

Introduction

Shortcoming of Sate-of-the-art ProductionData Analysis:

Inherent subjectivity.

It requires pressure data (bottom-hole or well-head).

Addresses individual wells rather than the entire field.

SPE 100562



5/28

Objective

Development of an integrated & comprehensiveproduction rate analysis technique:

Minimize subjectivity.

With reasonable repeatability.

With reasonable geological resolution.

Field wide production analysis

Addresses Entire Field (depletion, remaining reserve, etc.) Sweet spots.

Detect underperforming wells.

SPE 100562



6/28

Methodology

Methodology is demonstrated through applicationto a mature field in mid-continent U.S.

Wattenberg field in D.J. basin of Rockies. 137 wells used in the analysis.

Only publicly available production rate data was used

for analysis.

SPE 100562Introduction Objective Methodology Result and Discussion Conclusion


7/28

Methodology

An iterative approach that integrates:

Decline Curve Analysis

Type Curve Matching

Single-well Numerical Reservoir Simulation

& then applies all the findings to the entire field:

Fuzzy Pattern Recognition & Analysis

SPE 100562Introduction Objective Methodology Result and Discussion Conclusion


8/28

Methodology

SPE 100562

Step One:

Qi, Di, bEUR

EUR, b, h , K

S, A, , Xf

EUR, b, h ,K

S, A, , Xf



9/28

Methodology

Decline Curve AnalysisType Curve MatchingHistory Matching

TCM

DCA

HM

SPE 100562



10/28

Methodology

Step Two:

SPE 100562

SweetSpots

RemainingReserves

Underperformingwells

Trends inthe field

Fuzzy pattern recognition technique.



11/28


SPE 100562



12/28

Type Curve Matching

SPE 100562

b= 0.6 EUR = 204 MMCF

Decrease subjectivity in type curve matching

b= 1.69 EUR = 286.5 MMCF



13/28

Type Curve Matching

EUR30 = 532 MMCF

EUR30 = 401 MMCF

Adding uniqueness to the type curve match.

Actual Production Data

SPE 100562



14/28

Type Curve Matching

SPE 100562

To be used in History Matching



15/28

Type Curve Matching

SPE 100562

EUR30 =590.57 MMCF

EUR30 =116.09 MMCF



16/28

Reservoir Simulations

History Matching of production data using asingle-well radial numerical model.

Use the results of type curve matching

procedure as a guideline to achieve reasonablehistory match.

In order to achieve a reasonable match we have

to go back to TCM and DCA and iterativelymodify some parameters.

SPE 100562



17/28


SPE 100562



18/28


SPE 100562



19/28


SPE 100562

Monte Carlo Simulation



20/28

Combined DCA, TCM and HM

SPE 100562

Well ID Qi (MCF) Di b EUR (MMCF)

51231540400 9,500 0.064 0.773 376.87

51231556200 47,191 0.846 1.333 590.57

51231558900 8,177 0.084 1.14 378.27

51231559100 18,140 0.218 1.523 669.5


K (md) Xf (ft) A (acre) EUR (MMCF)

0.48 57.62 14.94 374.57

0.884 137.46 6.94 587.481

0.557 21.77 9.79 376.6

0.977 42.92 8.29 662.9

Type Curve Matching

Reservoir Simulator (history matching)

Monte Carlo Simulator

(EUR)



21/28

Reservoir Quality Index

Upon completion of the first step, we have a set ofreservoir characteristics that are reasonably close toreality, in quality and range.

The second part of analysis uses Fuzzy PatternRecognition technique to integrate the above informationin the context of the entire field.

SPE 100562



22/28


Fuzzy Pattern Recognition based on Longitude

Fuzzy Pattern

Recognition

based onLatitude

Low Relative

Reservoir Quality

Index RRQI

represents higher

quality reservoir

characteristics.

SPE 100562



23/28


SPE 100562

Note the

movement of

a well fromone RRQI to

another with

time.



24/28


SPE 100562



25/28

Reservoir Characteristics

SPE 100562



26/28

Remaining Reserves

SPE 100562



27/28

Remaining Reserves

SPE 100562



28/28

Conclusions

An Integrated technique has been introduced for

production rate data analysis.

The integrated technique uses DCA, TCM and HM in aniterative fashion to converge to a reasonable set of

reservoir characteristics. The integrated technique uses the fuzzy pattern recognition

with the results of above integration and produces 2D and3D maps of the filed for:

Reservoir Quality

Reservoir Characteristics

Remaining Reserves

SPE 100562


razi gaskari - tecnica integral de datos de produccion

Documents