A core calibrated log model workflow for shale gas and tight oil reservoirs

Transcription

1 A core calibrated log model workflow for shale gas and tight oil reservoirs Robert M. Cluff The Discovery Group, Inc. Elizabeth Roberts and Robert C. Hartman Weatherford Laboratories 2014 Rocky Mountain Section AAPG meeting, Denver, Colorado

2 Outline Types of workflows in common use today: deterministic or stochastic (probabilistic) Using core data to calibrate log models The 12-step workflow Several examples along the way

3 Stochastic or probabilistic workflows Guess at mineral and fluid volumes Compute log responses from a forward model Compare computed log responses with actual log data Satisfactory match? No Yes DONE

4 Minimize differences between actual logs and predicted logs Powerlog/Statmin example

5 Deterministic workflows Use a series of equations to predict minerals, TOC, and fluids from the logging measurements Equation parameters are assumed or calibrated to other data such as core measurements Every mineral, organic, and fluid component is independently solved for in a stepwise manner Often requires lumping components together to find a robust solution Can be zoned or global

6 Regression finds the optimal equations to thread the model through the points

7 Pro s and con s Stochastic Extremely powerful, solves for all components simultaneously with minimum error between prediction and observations Need to specify all the component end points for each & every measurement used in the model Also need to specify uncertainties both in component end points and in the measurements themselves Requires specialized software, models are not easily ported to other software even of the same kind Solutions tend to be opaque and difficult to reproduce

8 Deterministic Easy to document and understand ( transparent ) Easily implemented in common software ( transportable ) But, they are typically less flexible They require information independent of logs to find the model equations ( calibrated to core) May be zoned or global, but rarely allow parameters to float from foot-to-foot to find the optimal solution everywhere Like a recipe this is how you cook a log! We ll present a deterministic workflow today.

9 Why use core data for model calibration? Core is often our best (perhaps only) ground truth on what is actually in the subsurface Accurate and detailed information on mineralogy, TOC content, porosity, permeability, and fluid saturations Can measure gas (or oil) storage capacities and apparent gas content, both free and adsorbed Cores allow direct determination of petrophysical properties of interest; whereas logs measure something different and we INFER those properties from the geophysical data

10 12-step workflow Start with the basics: Load, QC, and edit data Make it consistent between wells: Environmentally correct and normalize the logs Bring in the core data: Import/merge core data and get it precisely on depth Assess the fluid saturations: Compare as-received core bulk density to the logs Assess your sampling density: Do you have enough data to calibrate a model with confidence?

11 1. Load, qc, & edit data Basic to all log analysis work flows, not just shales. Load data Splice log curves Null tool pickup Convert units Validate digitizing accuracy

12 2. Environmentally correct & normalize log data In a perfect world, this would not be necessary Environmental corrections are necessary, but insufficient Normalization fixes the residual errors

13 Haynesville example, after GR curves are corrected and normalized

14 Raw GR Curves Mancos Shale, Piceance Basin

15 Normalized GR Curves Mancos Shale, Piceance Basin

16 Raw GR Curves Wolfbone, Delaware Basin

17 Normalized GR Curves Wolfbone, Delaware Basin

18 Porosity log normalization Bulk density logs often need a tight side shift (~20% of logs) Usually due to poor calibration or error in conversion of density porosity to bulk density Constant offset of DRHO curve from zero a common symptom Neutron logs very commonly need to be normalized (>50% of logs) Tight side shift or two-point gain/shift depending on use of logs Lots of uncorrected environmental effects and differences between logging vendors Sonic logs rarely need to be normalized, simple shift usually suffices But they commonly need de-spiking and editing

19 3. Import/merge core data & depth shift Merge in the core gamma scan Depth shift, honor the physical breaks Don t interpolate values, only depths! Precise depth shifting is critical!! Keep an audit trail! L = C + x Convert core depths to log depth, then import the data

20 4. Compare as-received core vs. log bulk density core either imbibed fluid or was invaded/flushed core expelled fluid or dried during handling

21 5. Assess sampling density & establish appropriate zonation n = (Z α 2 σ/b) 2 B = Z /2 * σ α / n n number of samples required (or available) Z α/2 Z-value of the desired confidence interval; e.g for 95% confidence interval σ standard deviation of the population B required tolerance (+/-) in determination of the mean value of the property

22 Workflow, part II Calibrate the lithology model: Develop a set of equations to compute all the mineral components & TOC, calibrated to the core XRD and TOC data Convert from weight % to volume %: Determine appropriate component densities to convert from weights to volumes, optimized to the measured total grain density Compute the kerogen corrected porosity: compute the total porosity from the density log using the optimal grain density and TOC density Convert grain volumes to bulk volume: bulk volumes are reduced by the porosity. TOC is included in the grain volume.

23 6. Compute bulk mineralogy and TOC in weight % Regression analysis: simple, multiple, linear, and non-linear For TOC we also use deltalogr methods Can even find the optimal LOM in some cases in the absence of Ro or RockEval data Consolidate minerals as required to meet limitations of the available log data set Combine K-feldpar and plagioclase Combine all clay minerals into total Vclay Combine calcite, dolomite, siderite into VCO 3 unless a GOOD Pe is available

24 XLStat

25

26 Regression of variable XRD_CLAY: Summary of the variables selection (Variable XRD_CLAY): No. of variables Variables MSE R² Adjusted R² Mallows' Cp Akaike's AIC Schwarz's SBC A 1 GRC GRC / ND_DIFF PEF / GRC / ND_DIFF The best model for the selected selection criterion is displayed in blue

27 Results

28 TOC estimators GR, bulk density and DT Combinations thereof (multiple regression) deltalogr Requires knowledge of thermal maturity XRD weight percentages commonly ignore TOC, since it is X-ray amorphous Weight % of the matrix fraction need to be adjusted downwards to account for TOC

29 How many predictor variables are required?

30 Vol i Wti ρb = * 100 ρ gi 7. Convert weight % to volume % with optimized individual grain densities Vol i = Wti 100 * ρ ρ b gi Need to know the grain density of each individual component, ρ gi, and the total bulk density Weight fractions come from the equations developed in step 6 These are GRAIN VOLUMES, not bulk volumes

31 8. Compute TOC corrected porosity φ T = W ρ toc ma ρ b ρ ma * W toc + 1 ρtoc ρ ρ ma fl Method of Sondergeld et al, 2010, SPE , eqn 15 ρ ma is the average inorganic grain density at each depth step ρ TOC is the average TOC density and varies with organic matter type and maturity ρ fl is the average fluid density and can be either held constant in a zone or varied continuously as a function of Sw (which requires an iterative solution)

32 Miller, Roche & Cluff, 2013

33 9. Convert grain volumes to bulk volumes BV = Vol * (1 φ ) i i T Grain volumes are often reported by labs from XRD data Logs measure bulk volumes, so you need to correct for porosity There is a small error in the stepwise conversion of XRD wt% to log wt% to grain volume% to bulk volume% If you are really concerned, you can iterate on a solution at this point to minimize errors

34 Workflow, part III Compute water saturation and free gas (oil)-inplace: uses the total porosity and conventional Sw calculation method, with Rw and m tuned to the measured core Sw s Compute adsorbed gas capacity: determine Langmuir volume vs. TOC relationship and compute gas storage capacity of the organic fraction Correct the free gas volumes for volume occupied by adsorbed gas, then compute total gas storage capacity and gas-in-place.

35 10. Compute water saturation, free oil/gas capacities

36 Basic volumetric equations G free = * φ *(1 S ) ρ * B b T g w Scf/ton, by convention GIP = 43.56* A* φt * h*(1 Sw) B g MSCF, for area in acres OIP = 7758* A* φt * h*(1 Sw) B oi STB, for area in acres

37 11. Establish Langmuir parameters and compute adsorbed gas capacity G ads = V L * P P+ P L

38 12. Correct free gas volumes for adsorbed gas & compute total gas capacity G = G (C* G / B ) + G total free ads g ads Free gas calculation overshoots on the gas storage capacity because the porosity includes pore space that is already occupied by adsorbed gas Density and volume occupied by the sorbed phase can only be approximated, but it approaches the liquid density of gas ( g/cm 3 ) Correction is significant, >10% of total gas storage capacity Details in Ambrose et al 2010 (SPE )

39 Simplified Conceptual Model Old Methodology Void space measured by porosity measurement + Sorbed mass measured by adsorption experiment = Total GIP New Methodology Void space measured by porosity measurement + Sorbed mass measured by adsorption experiment - Free gas volume taken up by sorbed gas = Total GIP

40 Conclusions Our proposed deterministic workflow is: Simple and straightforward (KISS principle); Transparent and easily documented; Transportable between software packages; Fit-for-purpose, easy to update the model as additional data are acquired as a play evolves; and does not require require advanced logging suites, custom software, or tie you to any particular analyst.