Modelling Workflow Tool

Transcription

1 A Reservoir Simulation Uncertainty ty Modelling Workflow Tool Brennan Williams Geo Visual Systems Ltd

2 Contents 1. Introduction 2. Simulation model overview 3. What are the problems? 4. What do we need? 5. The tool 6. Case study 7. What comes next?

3 1. Introduction describe the ongoing development of a software tool for uncertainty modelling and history matching in the reservoir simulation discipline i of the oil and gas industry. brief overview of simulation models over the last decade what are the problems? data management etc. What do we need? aims of the tool run manager, uncertainty modelling, data analysis case study uncertainty modelling example what comes next? history matching, support for other simulators

4 2. Simulation Model Overview computer model used to predict the flow of fluids (typically, oil, water, and gas) through porous media dominated by finite difference simulators Eclipse, VIP some finite element and streamline simulators Output grid geometry initial grid property data one value per cell in the grid porosity,permeability etc recurrent grid property data one value per cell in the grid for each report timestep pressure, oil/water/gas saturations plot vectors e.g. production rate for each well for each plot timestep

5 2. Simulation Model Overview Then. built a single (incorrect) simulation model to describe the reservoir. manually history match this single model use the matched model in a series of prediction runs to compare different field production scenarios. Now. Build multiple models to gauge uncertainty history match multiple models prediction runs using multiple history matched models

6 2. Simulation Model Overview 1992 small model, cells, 15 year simulation, 300 plot vectors, 20MB

7 2. Simulation Model Overview 1998 medium model, 200,000 cells, 150 wells, 2,000 plot vectors, 70MB

8 2. Simulation Model Overview 1998 small model, 60,000 cells, 600 wells, 18,000 plot vectors, 127MB

9 2. Simulation Model Overview 2002 very large model, 3,000,000 cells, 800MB

10 2. Simulation Model Overview 2003 medium model with coarsening and local grid refinement 120,000 cells, 200MB

11 2. Simulation Model Overview 2006 large model with nested LGR, 300,000+ cells, 12,000 plot vectors, 1.3GB

12 2. Simulation Model Overview 2006 large model, 300,000 cells, 300+ wells, 360,000 plot vectors, 1.8GB

13 3. What are the problems? Single Model only one representation of an unlimited number of possible models that match reasonably well the known history. no understanding of uncertainty in a single model

14 3. What are the problems? Multiple Models. Models are getting bigger file size issues runtime issues Data analysis How do we model uncertainty in as few a number of simulation runs as possible?

15 3. What are the problems? Model size of 200,000 cells 40 wells 2,000 plot vectors 500MB output files per run 9 uncertainty variables with 3 values each (low,mid,high) = 3 9 or 20,000 runs, 40,000 hours (4 years) 2 hours per run and 10 TB data 2*9+1=19 Tornado runs = 38 hours runtime, 10GB data

16 4. What do we need? multiple models to model uncertainty Engineer/user designed workflow - capture steps in the simulation study process select/define independent/control variables Choice of algorithms to use to change our control variables run/deck generation and submission i.e. a run manager data analysis tools assisted history matching simulator independent

17 5. The tool - rezen Phase 1 - Run Manager Phase 2 - Data Analysis open box Phase 3 - Uncertainty Modelling Phase 4 - History Matching ongoing

18 5. The tool data hierarchy terminology ensemble : a set of related simulation decks varying around a core model deck : an individual simulator dataset or run (both input & output files) variable : a simulation parameter whose uncertainty or sensitivity we wish to investigate deck vector : plot vector imported from simulation output files (e.g. FOPT) ensemble vector : set of related deck vectors, one for each deck

19 5. The tool run manager Run Manager manage multiple reservoir simulation runs supports Eclipse manage the submission i of decks to the simulator queues provides convenience tools such as scan simulation input & output files (including binary) conversion tools (binary to text) built-in i diff between related.data files

20 5. The tool uncertainty modelling Uncertainty Modelling supports the engineer/user designed uncertainty modelling workflow select uncertainty modelling algorithm, define ensemble variables and build an ensemble control file with directiveses generate simulation decks and submit decks to the simulator queues built-in diff utility between related decks plots of objective function vs generated ensemble variable values

21 5. The tool uncertainty modelling Uncertainty modelling algorithms Discrete cases e.g. create n runs by specifying n values of each variable. Combination e.g. create n1*n2* runs by specifying n i values for the i th variable Tornado method or one-at-a-time uncertainty analysis. Requires 3 discrete values (low, min, high) of each variable. Monte-Carlo simulation. User can specify continuous or discrete distributions which are randomly sampled for each run. User specifies the number of runs to do. Plackett-Burman experimental design. Requires "+1" and "1" values of each variable, but it's usually a good idea to test for curvature, so run an allzero case too.

22 5. The tool uncertainty modelling Multiple Scenario Approach need to identify most significant reservoir uncertainties and ranges varying one-parameter-at-a-time (Tornado) is a starting point then a reliable method for combining parameters in an efficient set of simulations is needed (experimental design) and then the ability to use statistics to gauge the impact of the results

23 5. The tool data analysis Data Analysis import output vector data from simulator output files (for all decks) partially filter the data that is imported plot deck vectors (i.e. vectors for a specific deck) plot simulated with history (e.g. FOPT & FOPTH) plot ensemble vector against deck number or against ensemble variable simple statistics on individual vectors

24 5. The tool workflow description 1. create ensemble 2. define core model 3. define variables & generate range of values 4. generate & submit decks, import output data 5. plot vectors & analyze results differences between ensembles and/or decks may be: subsurface unknowns e.g. geological realizations etc. development scenarios e.g. infill location, water injection start date numerical issues e.g. model sizes, computational parameters etc.

25 5. The tool ensemble vector plot 1

26 5. The tool ensemble vector plot 2

27 5. The tool ensemble vector plot 3

28 5. The tool ensemble vector table

29 5. The tool ensemble vector plot vs deck

30 5. The tool ensemble vector plot vs ensemble variable

31 5. The tool ensemble variable plot

32 5. The tool ensemble variable table

33 6. Case Study - Infill Well Infill well vs no infill well 11 different geological realisations (perm & poro distribution) Want to identify what model variables are most significant Tornado algorithm on 9 variables to reduce to 4 variables Combine algorithm on 4 variables Allocate probabilities to variable values to generate an S-curve Select a number of models to use in future runs

34 6. Case Study Infill Well Project Workflow Task Workflow repeat for prediction case create base case define tornado values run hmatch cases visually inspect hmatch calculate incrementals identify most significant variables copy base case define combined values run hmatch cases visually inspect hmatch generate S-curve repeat for prediction case Stage 1 Identify Key Variables and Valid Cases determine P Stage 2 In Depth Analysis of Key Variables

35 6. Case Study Infill Well Project Workflow Stage 1 repeat for prediction case create base case define tornado values run hmatch cases visually inspect hmatch calculate incrementals identify most significant variables copy base case define combined values run hmatch cases visually inspect hmatch generate S-curve repeat for prediction case Stage 1 Identify Key Variables and Valid Cases determine P Stage 2 In Depth Analysis of Key Variables

36 6. Case Study Setup Ensemble Create ensemble variables and assign low-mid-high to each Variables will be used in tornado analysis to identify the big hitters

37 6. Case Study Setup Ensemble Directives enclosed in curly braces {formula 1-$residual_g gas} contain one statement or statements separated by semi-colons a statement is a command word optionally followed by arguments variables in the argument must be preceded by a $ value directive MULTIPLY formula directive MAXVALUE PERMX {value $highperm_leman} / / SWL {formula $residual_gas} / /

38 6. Case Study Setup Ensemble Edit the ensemble control file {value $residual_gas} insert directives instructions for Rezen about how to use the variables

39 6. Case Study Generate Decks Decks Created Each deck corresponds to one.data simulator input file.

40 6. Case Study Setup Ensemble Vectors imported simulation output files

41 6. Case Study Setup Ensemble Vectors View deck vectors

42 6. Case Study Setup Ensemble Vectors View ensemble vectors Ensemble vector is a collection of related deck vectors Time Plot

43 6. Case Study Setup Ensemble Vectors View ensemble vectors Tornado Plot

44 6. Case Study Visual History Match Identify cases that don t history match FGPR Left mouse button for drag graph Right mouse button for drag legend Press z in plot for zoom Decks Case D0001 aquifer size = 0 D0002 aquifer strength = 0 D0003 reservoir cont, polygons = A+B only D0004 carboniferous leman transmissibility = 0 D0006 Facies proportion = 30:70

45 6. Case Study repeat for infill well Repeat same process as before Data check ensemble control file Generate decks Submit decks Create ensemble vectors

46 6. Case Study Identify most significant variables R2 FGPT R2P1 FGPT These are only results from R2. In reality need to consider all 11 realisations together. R2P1 FGPT_F R2 FGPT_F

47 6. Case Study Infill Well Project Workflow Stage 2 repeat for prediction case create base case define tornado values run hmatch cases visually inspect hmatch calculate incrementals identify most significant variables copy base case define combined values run hmatch cases visually inspect hmatch generate S-curve repeat for prediction case Stage 1 Identify Key Variables and Valid Cases determine P Stage 2 In Depth Analysis of Key Variables

48 6. Case Study Stage 2 In depth analysis of key variables Key variables were found to be: Reservoir continuity Aquifer Size res_cont aqu_size Carboniferous facies proportion carb_facies High perm streak in Leman highperm_leman (client suggestion) Aquifer strength was also significant but it is directly related to aquifer size so it was discarded from further analysis. Full factorial analysis of the 4 variables = 3^4 = 81 cases/realisation But, from history match in stage 1 aqu_size downside, res_cont downside and carb_facies downside can be discarded. So, full factorial analysis of the 4 variables = 2x2x2x3=24 cases/realisation

49 6. Case Study Stage 2 Load output files and import vectors

50 6. Case Study Generating S-Curve Ensemble Variable low prob mid prob high prob Reservoir Continuity Aquifer Size Facies proportion L 0.30 M 0.35 H 0.35 High Perm Streak

51 6. Case Study Generating S-Curve Cumula ative Probab bility MonteCarlo (250 Runs) ED+ Tornado (23 Runs) Plackett-Burman ED (10 Runs) Cumulative Oil per Well (MMstb)

52 7. What comes next? run manager support for additional simulators integration with load balancers data analysis s-curve generation & display response surface plots user defined objective/goodness-of-fit functions history matching user defined history match variables, ranges, objective functions optimisation algorithms

53 7. What comes next?...data analysis response surface

54 7. What comes next?...data analysis objective function nwells ntimes Σ w j Σ w t abs( s t -h t ) n j=1 t=1 nwells ntimes Σ w Σ j abs(w t.h t ) n j=1 t=1

55 7. What comes next?...history matching no history match is unique aim is to get a model with good predictive capability define and implement workflow goodness of fit/ objective functions selecting history match variables & defining value ranges defining algorithms for adjusting the history match variables in the model to improve the match