Integration of Uncertain Subsurface Information For Reservoir Modeling. Ali Ashat Institut Teknologi Bandung Wednesday, March 5, 2013

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1 Integration of Uncertain Subsurface Information For Reservoir Modeling Ali Ashat Institut Teknologi Bandung Wednesday, March 5, 2013

2 Successful appraisal and development of geothermal fields requires the integration of uncertain subsurface information into reservoir simulation models.

3 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

4 Conventional vs Recent Paradigm in 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 VS 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

5 Geothermal Reservoir Model Simulation 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 years)

6 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

7 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, April 2005

8 Factors controlling porosity Porosity versus rock type. Fracture and alteration related porosity Porosity versus depth trend Paper from Proceedings World Geothermal Congress 2005 Antalya, Turkey, April 2005

9 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. Paper from Proceedings World Geothermal Congress 2005 Antalya, Turkey, April 2005

10 2. Conceptual Model Heat source Reservoir Fluid

11 3. GRIDDING RESERVOIR SIMULATION Gridding the reservoir model Apply "Distributed Parameter Approach" Rock and fluid properties are taken into account FTTM-ITB/Nenny_2009

12 GRIDDING FOR COMPUTER MODEL Model 1-D Flow Model 2-D Flow FTTM-ITB/Nenny_2009

13 GRIDDING Model 3-D Flow FTTM-ITB/Nenny_2009

14 GRIDDING Lateral and horizontal slice of the grid system. 3D Model Reservoir

15 PT Distribution

16 Depth 4. Matching PT at Natural State Condition Pressure Well XXX Temperature

17 5. Matching Pressure at Production History

18 6. Reservoir Performance Forecasting Forecast for electrical capacity 110 MW and Sw = 0.3.

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

20 Case Study: Kamojang by Sulaiman(1982)

21 Case Study: Kamojang Geothermal Field

22 Case Study: Kamojang Geothermal Field

23 Case Study: Kamojang Geothermal Field

24 Case Study: Kamojang Geothermal Field

25 Case Study: Kamojang Geothermal Field

26 Case Study: Muaralaboh Geothermal Prospect Structure Setting

27 Case Study: Muaralaboh Geothermal Prospect Muaralaboh Conceptual Model

28 Geothermometry temperature Case Study: Muaralaboh Geothermal Prospect

29 Case Study: Muaralaboh Geothermal Prospect Muaralaboh Heat Loss Model

30 Case Study: Muaralaboh Geothermal Prospect 3D Model Muaralaboh Permeability Structure Model

31 Case Study: Muaralaboh Geothermal Prospect Natural State Simulation Result

32 Case Study: Muaralaboh Geothermal Prospect Natural State Simulation Result

33 Case Study: Wayang Windu Geothermal Field

34 Case Study: Wayang Windu Geothermal Field Well Location in Wayang Windu

35 Case Study: Wayang Windu Geothermal Field Longitudinal cross section through the Wayang Windu field. Description of Wayang Windu model

36

37 Case Study: Wayang Windu Geothermal Field Matching PT at natural state condition

38 Matching Pressure Enthalpy at Production Matching

39 Case Study: Namora I Langit Geothermal Field 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.

40 Key Reservoir Parameters considered in the evaluation of the hypothetical reservoir 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

41 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.

42 Probability distribution of key reservoir parameters Paper from Proceedings World Geothermal Congress 2000 Kyushu - Tohoku, Japan, May 28 - June 10, 2000

43 Model Dependency of recovery factor from key reservoir parameters Paper from Proceedings World Geothermal Congress 2000 Kyushu - Tohoku, Japan, May 28 - June 10, 2000

44 Field abandonment pressure Cumulative probability curve for field generating capacity Paper from Proceedings World Geothermal Congress 2000 Kyushu - Tohoku, Japan, May 28 - June 10, 2000 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

45 Emerging Trends in Reservoir Model Simulation 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

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

47 Reservoir Simulator: TOUGH2, TETRAD, STARS-CMG 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.

48 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

49 Emerging Trends: Probabilistic analysis in reservoir modeling 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. Scheme of full factorial

50 DoE (Design of Experiment): Implemented in Darajat 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. Paper from WGC 2005, Darajat Geothermal Field Expansion Performance-A Probabilistic Forecast

51 Darajat Subsurface Uncertainties Table: Uncertainty ranges for Darajat reservoir main variables investigated in the Design of experiment methodology Darajat Uncertainties Variables Rankings Paper from WGC 2005, Darajat Geothermal Field Expansion Performance-A Probabilistic Forecast Plateau Length = *Swc e- 08*(Pore Volume) 5.387* (Res. Depth) (Rech/Dis Ratio)

52 Reservoir Performance Probabilistic Distribution Reservoir simulation P10, P50, and P90 models provide similar results as Monte Carlo P10, P50, and P90. P10, P50, AND P90 RESERVOIR SIMULATION MODEL PREDICTIONS Paper from WGC 2005, Darajat Geothermal Field Expansion Performance-A Probabilistic Forecast Paper from WGC 2005, Darajat Geothermal Field Expansion Performance-A Probabilistic Forecast

53 Emerging Trends: Inverse Modeling 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

54 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

55 Emerging Trends: Single or Dual Porosity 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.

56 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.

57 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

58 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.

59 Emerging Trends: Integrated Simulator 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.

60 Number of Make-up Wells - The relationship between Pressure inlet turbine and several parameters P Inlet Turbine

61 Emerging Trends: Integrated Simulator 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

62 End of presentation Thank you

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