Subsurface Reservoir Characterization for Enhanced Oil Recovery. Mark Tomasso 1

Transcription

1 Subsurface Reservoir Characterization for Enhanced Oil Recovery Mark Tomasso 1 1 Enhanced Oil Recovery Institute, University of Wyoming, Laramie,, Wyoming 82071, USA

2 Presentation Objectives FY2009 project summary by research h theme. Subsurface characterization: Calculating rock porosity from sparse data. Automatic facies estimation from wireline logs using neural networks: Application to the Minnelusa Fm. Interrogating the Minnelusa Fm.. database: Application to regional stratigraphy and structure. Future work into FY2010.

3 FY2009 Project Summary Reservoir characterization, assessment, and/or static geological modeling: Multiple field studies completed or ongoing. Tensleep, Minnelusa, Lakota, Wall Creek, Muddy, Shannon. Using old-style electric logs to estimate porosity for modeling. Outcrop characterization: Casper Sandstone, Sand Creek, Albany County ongoing. Phase 2 outcrop characterization (stratigraphy, structure, lidar). Minnelusa Sandstone ongoing. Phase 1 outcrop characterization planning. Applying outcrop analogs to the subsurface ongoing. Geological and forward seismic modeling. Paper accepted to AAPG Bulletin.

4 FY2009 Project Summary Subsurface geophysical interpretation and modeling: Field studies ongoing. Teapot Dome as needed. Interpretation of 3-D volume to enhance knowledge of WY reservoirs. Forward seismic modeling ongoing. Developing new techniques for quick and accurate 3-D forward seismic modeling of outcrop analogs and subsurface reservoir models. Paper accepted for AAPG Bulletin. Neural networking ongoing. Estimation of facies from wireline logs using neural networks. Regional subsurface work ongoing. Integration of EORI databases (Murrell, Reyes) with Forney database to analyze spatial distribution of Minnelusa stratigraphy and structure.

5 FY2009 Publications Papers: Tomasso, M., Bouroullec,, R., and Pyles, D. R., accepted, The use of spectral recomposition in tailored forward seismic modeling of outcrop analogs. AAPG Bulletin. Conference presentations: Tomasso, M., Bouroullec,, R. & Pyles, D. R., 2009, The use of spectral recomposition in tailored 3-D 3 D forward seismic modeling of outcrop analogs AAPG Annual Meeting, Denver, Abstract Volume, AAPG, Tulsa. Tomasso, M. & Wo, S., 2009, CO2-EOR potential of the Muddy/Newcastle Sandstone reservoir of Fiddler Creek Field, Weston County, Wyoming AAPG Annual Meeting, Denver, Abstract Volume, AAPG, Tulsa. Tomasso, M., Bouroullec,, R., Pyles, D. R. and Reyes, B. M., 2008, Bridging the scale gap: geological and seismic forward modeling of outcrop data AAPG International Meeting, Cape Town, Abstract Volume, AAPG, Tulsa. Plus 6 other presentations not directly related to EORI. However, lessons learnt can be used.

6 Calculating Rock Porosity from Sparse Data

7 The Problem Typically, y, we deal with smaller fields and operators: Sometimes only rely on public data at WOGCC. Old-style electric logs (SP/resistivity), paper scans. Core data may be available, but physical core generally not. How do we parameterize a reservoir model with such sparse petrophysical data? Investigate using resistivity logs to calculate porosity within the well. Integration with vshale logs derived from SP. This technique appears to work at all stratigraphic levels, from Cretaceous (e.g. Frontier Fm.) to Permian (e.g. Tensleep Sst), and in different fields.

8 Log Digitization Well C

9 Mud Filtrate Resistivity Need to calculate mud filtrate resistivities: Most well logs include mud density, ρ m, and mud resistivity, R m, for top and bottom hole. Need to calculate mud filtrate resistivity, R mf, at top and bottom hole from these values: K m mf = 10 R = K * R ( ( *ρ )) m m m Lowe & Dunlap, 1986 Then we apply these values to find out R mf along the well bore: R mf Rmf _ surface Rmf _ TD = Rmf _ surface * MD TD

10 Porosity from Resistivity Porosity is estimated from resistivity using a modification of the t porosity solution (e.g. Asquith & Gibson, 1982; Crain 1986): Where: a φ = ( R / R ) ( S ) R mf = mud filtrate resistivity, ohm-m. m. R xo = resistivity measured from log, ohm-m. m. S xo = water saturation of invaded zone; between 0.6 and 0.8. a = tortuosity exponent; 0.62 in sandstones, 1 in carbonates. m = cementation exponent; 2.15 in sandstones, 2 in carbonates. n = saturation exponent; 2 in sandstones and carbonates. mf xo c = resistivity type value; 1 for ILM/ILD logs, 2 for SN/LL7 logs. c n xo 1/ m High φ Low φ

11 Porosity from Resistivity

12 Porosity from Resistivity Porosity derived from resistivity logs gives a smoothed result when compared to core data: Sample length of tool. Moving average compared to core. Also includes significant micro- porosity in mudstones and shales. This can be filtered by combining derived porosities with vshale curves constructed from SP or gamma logs. At a certain cut off, e.g % Vsh,, effective porosity can be set to 0%.

13 Conclusions When n building a static geological model, it is essential to get the petrophysical parameterization as accurate as possible. Old fields have sparse data. Typically only have electrical logs, some core-based measurements, but no physical core. Developed an enhanced technique that estimates porosity from old-style resistivity logs: Calculate mud filtrate resistivities from log header data. Compute a down-hole mud filtrate resistivity gradient. Calculate porosity using a modified resistivity-porosity solution. Ideally, should be calibrated with in-field core measurements. Technique appears to work at multiple stratigraphic levels and in i separate fields.

14 Automatic Facies Estimation from Wireline Logs Using Neural Networks: Application to the Minnelusa Formation

15 Aims The Forney database provides us with an opportunity to analyze the Upper member of the Minnelusa Fm. in a regional sense. The Shier database provides us with an edited, normalized digital log suite (gamma and sonic; Shier, 1985). Can we integrate PMl core observations with the log data to automatically extract accurate facies data from the wireline logs using neural networks? Can this be applied in a regional sense?

16 Minnelusa Digital Logs Shier database comprises >6500 Minnelusa wells. Gamma and sonic logs digitized from above PMk to TD. Logs normalized and QC d to remove cycle skips, etc. Integrated with Forney database picks.

17 Minnelusa Digital Logs Logs undergo an internal QC at EORI. Clipped to top PMl.

18 Neural Networking Core examination allows for facies to be related to wireline logs. Knowing this, can we estimate facies from the logs? Unsupervised neural networking: 9 classes. Defines dolomite vs. anhydrite.

19 Comparison to Core Comparison of core to unsupervised neural net derived facies associations. Good depiction of carbonates. Possible differentiation of cemented vs. oil- bearing sandstones. Ongoing study. +6 ctl shift needed

20 Conclusions Core data provides the most information about the actual rock in a well. Not all wells have core. In the Minnelusa, there is a distinct relationship between core facies and a combination of gamma and sonic logs. Estimates of facies can be picked manually. Using gamma and sonic as input, estimates of facies can be made automatically using neural networking: Unsupervised neural networks give a result constrained to sandstone vs. carbonate vs. anhydrite, and may help with cemented vs. oil- bearing sandstone. The next step is to carry out networking constrained to facies observed in core supervised neural networks. Can we apply this on intra-field, inter-field, and regional bases?

21 Interrogating the Minnelusa Fm. database: Application to regional stratigraphy and structure

22 Aims The Forney database provides us with an opportunity to analyze the Upper Minnelusa Fm. in a regional sense. Coupled with the EORI and GIS databases (Murrell, Reyes), provides a powerful tool to interrogate spatial relationships within the Minnelusa. Mapping of horizons above and within the Minnelusa Fm. Minnekahta, Opeche, top and intra Minnelusa. Use QC d Forney database internal PMl picks.

23 Regional Top Minnelusa Forney database coverage over the bulk of the PRB. Majority of data points in NE PRB: Most Minnelusa drilling.

24 Top Minnelusa, Study Area Regional structural dip to SW at ~0.2. Structural high to the east: Black Hills Uplift. Through-going going NE-SW structure: Belle Fourche Arch.

25 Minnekahta-Minnelusa Minnelusa Isopach PMk-PMl PMl ispach illustrates significant incision in the top PMl. Assumption: Top PMk was deposited ~flat. Opeche-aged drainage, filled with mudstone. Most fields found on SW side of large Opeche incisions: Excellent strat trap.

26 Minnelusa Subcrop Map showing PMl subcrop directly beneath top Minnelusa unconformity. PMl stratigraphy dips and youngs to the SSE. Why? Hypotheses: Post PMl tilting and/or subsidence to SE? Progradational shingling of PMl to SE?

27 Minnelusa Subcrop Red Shale Marker

28 Minnelusa Structure General hypothesis: Tensleep reservoirs are structural traps. Minnelusa reservoirs are purely stratigraphic traps. Numerous NNW-SSE and SW-NE trending basement lineaments identified in the PRB. Act as controls on sedimentation and preservation. Do these lineaments also effect the PMl? Modified from Martinsen (2003), Marrs & Raines (1984)

29 Minnelusa Structure Dip azimuth map for top PMl. Defines a series of linear NNW-SSE and SW-NE features at top PMl level. Why? Hypotheses: Artifacts of POp incision? A previously unrecognized structural control?

30 Minnelusa Structure Dip azimuth map for top PMk. Also defines a series of linear NNW-SSE and SW-NE features in same position as top PMl. But top top PMk is flat? Localized offset due to compaction of POp mudstones? Why not everywhere? Evidence of basement structural grain localizing POp incision into top PMl? Fault zones easier to erode. Reyes, 2009, EORI-GIS database query on PMl API and salinity? Springen Ranch Rozet Weston/ Hat Creek Belle Fourche Arch S. Coyote Ck Goose Butte

31 Minnekahta Isopach General gradual thickening to E and SE. Basin subsidence. Mottled appearance: Artifact of gridding? Something more?

32 Minnekahta Isopach General gradual thickening to E and SE. Basin subsidence. Mottled appearance: Artifact of gridding? Something more? Local compaction of POp w/ PMk subsidence thickening? Sinkholes related to PMl? Sinkhole Paleocave in collapse the PMk, features, SE of Boyd, Ellenburger WY (nr. Four Group, Corners, Boonsville Weston Field, Cty). Texas. From Epstein McDonnell (2005) et al. ; original (2007) from Darton (1909) S PMl sinkhole N Sinkhole in PMl formed by anhydrite dissolution, Redbird Canyon, Custer Cty, SD. EORI TAB/Commission Modified Meeting, from Epstein rd June (2005) 2009

33 Conclusions A spatially constrained database allows interrogation of the Upper Minnelusa Fm. from regional to intra-field scales. The Minnelusa subcrop dips SSE relative to the regional SW dip: Either as a result of progradation to SSE or tilting/uplift and erosion post PMl,, or a combination of both. A structural grain related to major basement lineaments in the PRB is present within the Minnelusa Fm. Possibly localized position of Opeche incision into the top PMl. Explains many fractures observer in PMl cores. Provides a framework for application of detailed facies analysis on a regional scale using neural networks and the Shier digital wireline log database.

34 Future Project Summary Reservoir assessment, characterization, and/or static geological modeling: Multiple field studies. Tensleep, Minnelusa, Shannon, Wall Creek, Lakota, Muddy. Regional and near-field Minnelusa. Stratigraphic and structural architecture. Wireline log analyses. Automatic extraction of facies and petrophysical parameters. Application to regional and intra field PMl facies analysis. Outcrop characterization: Casper Sandstone, Sand Creek, Albany County ongoing. Phase 2 outcrop characterization (stratigraphy, structure), static modeling. Minnelusa Sandstone. Muddy Sandstone.

35 Future Project Summary Subsurface geophysical interpretation and modeling: Seismic interpretation for reservoir characterization - ongoing. Obtain 3-D seismic data over specific fields. Interpretation of amplitude and attribute images. Forward seismic modeling. Bridge the scale gap between outcrop and subsurface.

36 Acknowledgements State Legislature of Wyoming EORI and Affiliated Researchers B. Reyes, G. Murrell, L. Whitman, G. Thyne, J. C. Lorenz, S. Cooper, J. Steidtmann, V. Alvarado, S. Wo, P. Yin, C. Coolidge, M. Johnson. Software & Technology A. Tanner, G. Paige (UW College of Agriculture) External Affiliates G. Forney, D. Shier, B. Lewis, staff of USGS Denver, Shane True, R. Bouroullec, D. R. Pyles