integration of uncertain subsurface information for...

Ali Ashat

Institut Teknologi Bandung

Wednesday, March 5, 2013

Integration of Uncertain Subsurface Information For Reservoir Modeling

Successful appraisal and development of geothermal fields

requires the integration of uncertain subsurface information into

reservoir simulation models.

Outline

• Reservoir Modeling Insight

• Knowledge of Reservoir Modeling

• Case Study/Some Examples

• Emerging Trends in Reservoir Simulation

• Data requirements from 3G analysis for reservoir model simulation

Reservoir model simulation is

performed in more certainty

subsurface information

Geology, geochemistry, and

geophysics (3G) data are

acquired before building a

reservoir model

Used to assess reservoir

performance under different

scenarios of development

Conventional vs Recent Paradigm in Reservoir Model Simulation

3G analysis take reservoir

modeling into account

(3G+R)

Together with 3G analysis

confirm a geothermal model

conceptual

Can go without having a

complete 3G analysis

1. Data Analysis and Collection

2. Creating Geothermal Conceptual Model

3. Gridding (grid system).

4. Input data preparation

5. Reservoir modeling at natural state condition (Model validation and

data matching)

6. Reservoir modeling for history matching (Model validation and data

matching)

7. Reservoir performance forecasting (over the next 25-30 years)

Geothermal Reservoir Model Simulation

1. Data analysis and collection for reservoir simulation

• Geological data: rock type, geological structure (faults)

• Geochemical survey (type of fluid, upflow & outflow zone (fluid flow direction),temperature prediction, etc)

• Geophysical survey (geological structure, reservoir boundary or reservoir geometry, etc)

• Well measurements: Fluid properties (reservoir temperature, pressure, impurity fluids)and Rock properties (permeability, porosity, relative permeability, capillary pressure,etc), reservoir top & bottom, feed zones locations, well trajectory

• Previous reports and literature

Porosity Data (Darajat experience)

• Porosity is one of the critical factors in geothermal reserve estimation, as a majority of

geothermal fluid reserves in a vapor system are stored in the reservoir rock matrix porosity.

• Porosity study: petrography of blue-dye impregnated thin sections, X-Ray Diffraction

(XRD) and Portable Infrared Mineral Analyzer (PIMA).

• Core data are mostly available from the wells located on the field‟s margin, and very

limited from the field‟s center.

• A wireline porosity log estimate becomes critical where core data is not available.

• Integration of Schlumberger‟s Accelerator Porosity Sonde (APS) and Formation Micro

Scanner (FMS) pseudoresistivity were correlated with core data and provided porosity

estimates for the field center.

• The core porosity data were used as primary data for reservoir simulation, while wireline

log data were used to predict the range of porosity values.

Paper from Proceedings World Geothermal Congress 2005

Antalya, Turkey, 24-29 April 2005

Factors controlling porosity

• Porosity versus rock type.

• Fracture and alteration related porosity

• Porosity versus depth trend

Factors controlling porosity

Study from Rejeki, Hadi and Suhayati (Amoseas and ITB) implied that porosity is

mainly controlled by rock type and alteration processes.

Matrix porosity results exhibit a range of porosity by rock type from highest to

lowest: tuff, breccia, lapilli, lava (and intrusive) rocks. Low porosity lava and

intrusive dominates the center part of the field, while higher porosity pyroclastics

dominates the field‟s margins.

2. Conceptual Model

• Heat source• Reservoir• Fluid

RESERVOIR SIMULATION

Apply "Distributed Parameter Approach"

Gridding the reservoir model

Rock and fluid properties are taken into account

FTTM-ITB/Nenny_2009

3. GRIDDING

GRIDDING FOR COMPUTER MODEL

Model 1-D

Model 2-D

FTTM-ITB/Nenny_2009

Model 3-D

FTTM-ITB/Nenny_2009

GRIDDING

Lateral and horizontal slice

of the grid system.

3D Model Reservoir

PT Distribution

4. Matching PT at Natural State Condition

Pressure

Temperature

5. Matching Pressure at Production History

6. Reservoir Performance Forecasting

Forecast for electrical capacity 110 MW and Sw = 0.3.

Case Study: Kamojang Geothermal Field, First Model, Mountfourt(1979)

Case Study: Kamojang by Sulaiman(1982)

Case Study: Kamojang Geothermal Field

Case Study: KamojangGeothermal Field

Case Study: Muaralaboh Geothermal Prospect

Structure Setting

Muaralaboh Conceptual Model

Geothermometrytemperature

Muaralaboh Heat Loss Model

3D Model Muaralaboh Permeability Structure Model

Natural State Simulation Result

Case Study: Wayang Windu Geothermal Field

Well Location in Wayang Windu

Longitudinal cross section through theWayang Windu field.

Description of Wayang Windu model

Matching PT at natural state condition

Matching Pressure Enthalpy at Production Matching

• Status: No production data• What is the need of conducting a reservoir model simulation before

there is no production data?• A reservoir modeling is used to define the number of recovery factor

of the reservoir• Thus, the recovery factor becomes an output of the reservoir

simulation.• What is the recovery factor?

• The reservoir performance can be evaluated based on the resulting

recovery factor R, defined as:

• R = Ms/MIP

Ms = total produced steam during 30 years

MIP = initial fluid mass in place.

Case Study: Namora I Langit Geothermal Field

• Areal extension (Field limits)

• Elevation of reservoir top and bottom, defining the thickness of the reservoir.

• Reservoir Temperature

• Porosity: The average value of the porosity over the whole

reservoir thickness is used as characterizing parameter

• Permeability The assumed values represent the possible

range of the “average” permeability for the whole reservoir.

• Average Fracture Spacing

• Productivity index

Key Reservoir Parameters considered in the evaluation of the hypothetical reservoir

• Reservoir area, reservoir top and bottom, matrix porosity and

temperature (affecting fluid density) were treated as independent

variables for the purpose of calculating fluid mass in place.

• Simultaneously, porosity, temperature, fracture permeability, fracture

spacing, well productivity index, cold fluid influx, reservoir top and

bottom, extraction depth, and exploitation strategy were treated as

independent variables for the purpose of calculating the appropriate

recovery factor.

Key Reservoir Parameters considered in the evaluation of the hypothetical reservoir

Kyushu - Tohoku, Japan, May 28 - June 10, 2000

Probability distribution of key reservoir parameters

Model Dependency of recovery factor from key reservoir parameters

Field abandonment pressureCumulative probability curve for field

generating capacity

• a 90% probability of having a capacity of 175 MW or higher

• a 50% probability of being as high as 290 MW

• a 10% probability of exceeding 460 MW

Numerous 3D data to be collected: Database and Simulator

Probabilistic analysis in reservoir modeling

Inverse modeling

Single or dual porosity, Discrete Fracture Network

Integrated modeling

Emerging Trends in Reservoir Model Simulation

As technology advances, as well as increase in number of data, there is a

need to collect the numerous data sets in a database program which can

help interpretation for geoscience study

A database program to allow import of numerous data sets should be able

to combine many types of data in particular of geoscience data (raw

data/image or static model of geoscience)

Besides, a database program which also functions as a data storage should

be compatible to be exported into processing tool or simulator

Examples of database program: Jewel, Petrel, Leapfrog3d

Examples of processing tool or reservoir simulator: TOUGH2, TETRAD,

STARS-CMG

Emerging Trends: Numerous 3D data collected: Database and Simulator

a) TOUGH2 most popular, less expensive

It can allow import of static data from database program (only from Leapfrog3d)

b) TETRAD expensive software/less expensive than STAR CMG

It can‟t import static model (3G data)

c) STARS CMG expensive software

It can allow import of static model (3G data) from database programs (Petrel

and Jewel).

It can help to understand integrated geothermal system based on geological,

geochemistry, and geophysics survey in a more advanced features and images.

Reservoir Simulator: TOUGH2, TETRAD, STARS-CMG

„A communication‟ between a database program and a reservoir simulator

should be good for it is very helpful and of time-saving to learn the

whole system of geothermal

Probabilistic in full factorial scheme

In probabilistic analysis, we take many possibilities

of the reservoir parameters into account.

Various uncertainty reservoir parameters create

many possibilities in reservoir model.

Vice versa, many models which contained of many

possibilities in the reservoir parameters can be built.

It is nearly impossible to run all possible models

(time-consuming and inefficient).

Moreover, the uncertainties can significantly

overestimate or underestimate a resource potential.

Emerging Trends: Probabilistic analysis in reservoir modeling

Scheme of full factorial

Hence, a technique called DoE (Design of

Experiment) is applied to capture only relevant

uncertainties.

This process systematically identifies, ranks, and

quantifies key parameters affecting field performance.

DoE (Design of Experiment): Implemented in Darajat

Paper from WGC 2005, Darajat Geothermal Field Expansion

Performance-A Probabilistic Forecast

Table: Uncertainty ranges for Darajat reservoir

main variables investigated in the Design of

experiment methodology

Darajat Subsurface Uncertainties

Darajat Uncertainties Variables Rankings

Plateau Length = -28.785 + 47.125*Swc + 1.109e-

08*(Pore Volume) – 5.387* (Res. Depth) + 15.375

(Rech/Dis Ratio)

Reservoir simulation P10, P50, and P90

models provide similar results as Monte Carlo

P10, P50, and P90.

Reservoir Performance

Probabilistic Distribution

P10, P50, AND P90 RESERVOIR SIMULATION

MODEL PREDICTIONS

Reservoir modeling is a repetitive activity to change reservoir parameters

(permeability, porosity, etc.) for matching model output with observed data.

In forward modeling, we change the reservoir parameters in order to match

the model simulation output and real data.

Due to high uncertainty of the reservoir parameters, the process of

matching become rather difficult since we deal with many possibilities in

the reservoir parameters.

Hence, inverse modeling is developed to estimate reservoir parameters based

on measured/observed data.

Lately, the inverse modeling is considered to be more inefficient and

unrealistic if we deal with complex model

Emerging Trends: Inverse Modeling

Inverse modeling is more suitable for simple model over short range of

parameter values

It is also used for smoothing models in history production matching

Emerging Trends: Inverse Modeling

Single porosity: permeability and porosity of matrix

Dual porosity: permeability and porosity of matrix and fracture

Single vs dual porosity?

Dual porosity modeling more represents the real condition of geothermal

reservoir where fracture roles as fluid transmitting while matrix as fluid

storage

Dual porosity modeling is more difficult than single porosity modeling

The difficulty is primarily caused by determining the value of additional

parameters, i.e. spacing, fracture density, permeability and porosity fracture.

Emerging Trends: Single or Dual Porosity

Study from Darajat Geothermal Field:

Integration of the core study, APS, FMS and thin sections indicates that

the majority of the Darajat reservoir is composed of a dual porosity

environment in which fractures form the main conduits for fluids to

move (high permeability, more alteration) and the rock matrix has very

low permeability and is less altered.

Emerging Trends: Single or Dual Porosity

Emerging Trends: Discrete Fracture Network (DFN)

Unstructured and structured Grid

system (Voronoi/PEBI-based grid)

Fractured/fault is discretized

explicitly.

Introduces two different domain

(geometrical & computational

domains)

Can be embedded into TOUGH2

Emerging Trends: Integrated Simulator

The necessity of having integrated simulator:

• The determination of an optimum turbine inlet pressure is one of the key

solutions to the optimum of geothermal field development.

• It needs an integrated understanding of both technical and economic aspects

which covers from the reservoir, wellbore, pipeline & steam field facilities,

and power plant modeling.

• Each aspect contributes to the number of optimum turbine inlet pressure.

This can be explained for its impacts on the whole project of geothermal

from upstream to downstream business.

• Therefore, an adequate assessment of turbine inlet pressure is only possible

if the evaluation of each aspect has been carried out in advance.

The „How‟

• To determine an optimum turbine inlet pressure, the procedure is accomplished by

combining reservoir modeling, power plant modeling, and economic modeling.

• Power plant modeling analyzes the relationship between two variables, i.e. specific

steam consumption and turbine inlet pressure. Reservoir modeling deals with a

relationship between steam availability and various turbine inlet pressures.

• The economic modeling yields the most feasible project economic indicator.

• Reservoir modeling is also used to analyze the sensitivity of the uncertainty

parameters within the reservoir and the effects of field development strategy to

the reservoir performance.

The relationship between Pressure inlet turbine and several parameters N

P Inlet Turbine

Current Condition:

• Current reservoir simulator such as

TOUGH2 has no well modeling

modules.

• BHP/BHT is a function of mass rate of

wells and pressure/temp. at the grid

blocks.

• WHP & WHT constraints can be

coupled with the reservoir model through

wellbore simulation module in the

TOUGH2++.

• Lot of works to do

End of presentation

Thank you

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