Numerical Well Testing A Method for 3D Model Validation ... · PDF file3D Model Validation...

DELIVERING KNOWLEDGE. DEVELOPING COMPETENCE.

Numerical Well Testing A Method for 3D Model Validation using Pressure

Transient Tests

USE OF THE INTEGRATED SOFTWARE SYSTEMS FOR MODELING HYDROCARBON RESERVOIRS

Authors: Vasile Badiu and Florin Vasile Badiu - I.C.P.T. Câmpina Presented at PETROM Symposium, 24-26 may 1999, Câmpina Published in Revista Românå de Petrol, Nr. 2, April - June 2002

Translated from Romanian by Florin Vasile Badiu, GTA at UTA, Texas, February 2005

© 2008 PetroSkills, LLC. All rights reserved.

Static and Dynamic Models

–  Principles: •  We need to build Static and Dynamic Models in an appropriate

Integrated Reservoir Modeling (IRM)Procedure

•  The model is never better than the data, the people, the tools and the techniques used (to obtain it)

•  At all times, the model is NON-PERFECT

•  The model is good, as long as we “believe” in it

•  History matching is finished only when you run out of time, money or both


Integration - Concept

Data

Tools Technology

People

Integration


Static and Dynamic Models in the past In The Industry

–  Analog and Physical Modeling - until 1972

–  2D Numerical Reservoir Simulation - from 1970 until 2001

–  2D Streamline Simulation - from 1972 until 2001

–  3D&3P Numerical Reservoir Simulation - from 1985 until 2001

–  Integrated Reservoir Modeling using Integrated Software System - from 1993


Integrated Reservoir Modeling

RESERVOIR SIMULATION 3D FLUID DISTRIBUTION

GEOLOGICAL MODELLING 3D FRAMEWORK MODEL

SEISMIC INTERPRETATION BASE MAP

GEOLOGICAL MODELLING 3D BLOCK DIAGRAM

STREAMLINE SIMULATION 2D STREMLINE DISTRIBUTION

WELL LOG ANALYSIS SYNTETIC LOG

PROJECT DATABASE

MANAGEMENT

MAPPING BASE MAP

SEISMIC INTERPRETATION SECTION FROM A 3D SURVEY

WELL TEST ANALYSIS LOG-LOG PLOTS

UPSCALING 3D SIMULATION MODEL


Multidisciplinary Team

The key is to find the way for an Integrated

Reservoir Modeling with multidisciplinary team

and 3D model validation using additional well test

data and System Procedures.


An Integrated Reservoir Modeling Validation Procedure

Data Acquisition and Analysis

Static Model Building

Dynamic Model Building

Reservoir Simulation

Prediction Evaluations

Integrated Reservoir Modeling Study Report

Validation Well Tests


Reservoir Simulation today and tomorrow

Analysis Floviz, Office

Grid building Flogrid

PVT-data PVTi

Well data preparation Schedule

Rock data SCAL

Wellbore hydraulics

VFPi

Well Testing Weltest

Geological model GEOFRAME OpenWorks

Irap RMS Petrel

Processing Eclipse 100-500, Frontsim

History Matching (SimOpt, Weltest)

Well events Production data

Well test data Well logs

Interactiv Pet. Saphir, Topaze Mbal, Fieldpro

Economic Evaluation Peep, Merak


What is Validation Procedure Time?

History Match 60%

Prediction 15% Model Construction

20%

Presentation & Documentation 5%

History Match Is the Longest

Portion of Validation


Objectives and deliverables

!  Objectives: –  to validate and correct the near wellbore regions (3D model)

from the simulation models when well test data are available.

!  Deliverables –  to create an easy to follow workflow for matching numerical

well test with field data. For this workflow are used the applications: Flogrid, ECLIPSE 100, Weltest, SimOpt (Schlumberger Products)


The Method

It consists of four steps: 1.  Analysis of test data using state-of-the-art analytical methods. 2.  Extracting the area of influence of the well test from the

available numerical simulation model and modifying the grid to allow for transient-level analysis.

3.  Modifying the properties in the extracted area until measured pressure and derivative are matched with model response.

4.  Verify the production history of all wells using the new full-field model. Repeat step 3 if necessary to obtain consistent results.


From SPE 95905

The Method


Test model

!  A single vertical well was considered with the perforations from 994 m to 1024 m.

The field data were simulated using a very

fine model with 1.2 million cells.

A cell has 2mx2mx1m.

The numerical well test was simulated using a

726 cells model.

A cell has 36mx36mx5m.


Static properties

Variogram:

Horizontal Range = 100 m

Vertical Range = 2 m

Sill = 500

+

Porosity = 0.2, Permeability populated using well data.


Static properties

Permeability Mean ≈ 30 mD Permeability Mean ≈ 43 mD


Oil Production

Pressure Coarse Model

Fine Model

Simulation procedure

3d Model

+

Well target: Oil Rate 50 m3/day

FVF & Viscosity

Dynamic properties

Relative permeability curves


Weltest comparison

Reservoir Datum Depth (TVD): 994 m

Top of Reservoir Depth (TVD): 994 m

Initial Reservoir Pressure: 200 bar

Fine Model Analysis

Av. Permeability: 30.9 mD

Total Skin: -0.1

Average Well Pressure: 171.4 bar

Initial Res Pressure: 200 bar

Well perf length: 30.0 m

Coarse Model

Fine Model – Field Data


Matching kh vs. kv

History match the pressure data by changing the vertical and horizontal permeability of the coarse model.

Real Data (Fine Model) Coarse Model Data


Results

Coarse Model

Fine Model

Oil Production

Pressure Coarse Model

Fine Model


Case Study – Horizontal Well

► Type of Well: Horizontal

► Type of Test: Drawdown (22 days )

► Initial Pressure: 235 bar

► Bubble Point Pressure: 149 bar

► Number of Cells: 9568 (23x13x32)


The Well Test Data

Drawdown Test

Oil Production

Pressure

Drawdown pressure & Oil Flow Rate


Horizontal Well Model

The Volume of influence of the horizontal well extracted from the available numerical

simulation model Horizontal well

Permeability Log


Permeability Mean ≈ 4.26 mD

The model in

► The numerical well test was simulated using a

9568 cells model (23x13x32).

► The model cells has: ►  DX= 16 m ►  DY = 14 m ►  DZ = 5 m.


History matching

Simulated Data

Observed Data

Bottom Hole Pressure

► Average permeability before history matching: 4.26 mD ► Average permeability after history matching: ≈ 2.1 mD


Weltest Results Log-Log Drawdowns

Field Data Analysis

Simulation Data Analysis


Measured Data – Reservoir Answer

Simulation Model

Welltest comparison Log-Log Drawdown

Measured Data – Reservoir Answer


Unit Scale Simulation Model

The Simulation Model has 25000

cells: ►  NX = 25 ►  NY = 25 ►  NZ = 40

3D Reservoir Unit Fluid Distributions


Conclusion of Example of IRM► The traditional history matching for a full field model can

take months. A quick validation of the model with Numerical Well Test (NWT) will take days allowing changes in the model before going into a full field study.

► NWT is a useful and practical method for 3D Model Validation and near wellbore modeling for mature fields.

► NWT enables the use of the additional information obtained from well tests in reservoir description & performance predictions.

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