From Nanopore to Seismic Scale: the Role of Organic Matter in Vaca Muerta Shale Oil Productivity and Sweet Spot Prediction in Rincón de Aranda Block*

Transcription

1 From Nanopore to Seismic Scale: the Role of Organic Matter in Vaca Muerta Shale Oil Productivity and Sweet Spot Prediction in Rincón de Aranda Block* Denis Marchal 1, A. Perez Mazas 1,3, C. Naides 1, F. Sattler 1, J. Erlicher 1, G. Köhler 1, E. Nigro 1, S. Sommacal 2, and A. Fogden 2 Search and Discovery Article #42011 (2017)** Posted February 20, 2017 *Adapted from oral presentation given at Latin America & Caribbean Region Geoscience Technology Workshop, Buenos Aires, Argentina, November 16-18, 2016 **Datapages 2017 Serial rights given by author. For all other rights contact author directly. 1 Pampa Energía, Buenos Aires, Argentina, South America (denis.marchal@pampaenergia.com) 2 FEI Oil and Gas 3 Now in YPF Abstract An increasing number of studies demonstrate the key role of organic matter in the productivity of shale plays. Integrated results from the first shale oil well in Rincón de Aranda (RDA) block confirm this strong relationship, but current understanding of hydrocarbon storage and flow in the matrix and fractures of shales is still insufficient to predict recovery. In order to understand the specific role of organic matter for porosity and oil saturation in a shale oil reservoir, we image shale pore networks at microto nanoscale and quantify relative petrophysical properties using an integrated workflow of CT/FIBSEM 3D imaging of core subsamples. From well logs and laboratory data, we observed a strong correlation between TOC and Sonic measurements for a large collection of shale oil wells and calculated a TOC-DT function. Using neuronal network and multiatribute workflow, we applied this function to the Transit Time cube calculated from a trace inversion of the 3D seismic data available for the RDA block and obtained a calibrated TOC cube. If the search for the sweetspot(s) in an area should be multicomponent (TOC, reservoir thickness, overpressure, etc.), the TOC cube should be considered as a key element for productivity prediction not only in Rincón de Aranda but also all over the Vaca Muerta oil window.

2 Reference Cited Sommacal S., A. Fogden, B. Young, W. Noel, A. Arena, L. Salazar, T. Gerwig, C. Qianhao, A. Kingston, D. Marchal, A.M. Perez Mazas, C.H. Naides, G. Köhler, and M. Cagnolatti, 2016, 3D multiscale imaging of the distribution of pores, organic matter and oil in place in Vaca Muerta shale samples: Unconventional Resources Technology Conference, San Antonio, Texas, USA, 1-3 August 2016, MS-URTeC.

3 Moving toward the Prediction of Unconventional Plays: Lessons Learned from Tight and Shale Reservoirs in the Basin

4 From nanopore to seismic scale: The role of organic matter in Vaca Muerta shale oil productivity and sweetspot prediction in Rincón de Aranda block 17/11/2016 Denis MARCHAL, Ana PEREZ MAZAS*, Claudio NAIDES, Federico SATTLER, Jorge ERLICHER, Guillermina KÖHLER, Esteban NIGRO (Pampa Energía), Silvano SOMMACAL, Andrew FOGDEN (FEI Oil and Gas) * Now in YPF

5 From Nanopore to Seismic Scale - Play Map & Block location RDAx1001

6 From Nanopore to Seismic Scale - RDA.x-1001 location RDA.x-1 T4 T5 Structural map Base Vaca Muerta Fm. En-échelon fault trends T3 T2 RDA.e-2 RDA.x-1 RDAO.x-1 Rincón de Aranda T1 RDA.x-1001 CPC.x-1

7 From Nanopore to Seismic Scale - Sweet Spot definition Critical Factor Index with Curvature: CFIc CFIc = c1*evm(n) + c2*cot(n) + c3*ro(n) + c4*curv(n) c1+c2+c3+c4 X(n): normalized parameter c1, c2, c3 y c4: weighting coeficientes (this run, c1=c2=c3=c4=1) evm(n) RDA.e-2 RDA.x-1 N COT(n) Ro(n) RDA.x x Grid km Curv(n)

8 From Nanopore to Seismic Scale - 3D Structural Model & RDA.x Pampa energia

9 A1 A2 A3 Stage 1 4,026 sx Stage 2 5,016 sx Stage 3 3,515 sx Stage 4 3,401 sx From Nanopore to Seismic Scale - RDA.x-1001 result summary TR T R Transecta T5 PESA Perf Frac PLT A32 0% 20% 40% 60% 80% T4 A31 A22 PLT 1 PLT 2 PLT 3 PLT 4 PLT 5* PLT 6 A21 T3 A21 T2 A11 T1 FEI Sample

10 From Nanopore to Seismic Scale - Sample & Subsamples Original Sample: Subsamples: V1: 4.3x6.7mm V2: 5x29.3mm V3: 5x18.0mm

11 Electron Microscopy X-ray Micro-CT tomography From Nanopore to Seismic Scale - Shale Workflow V1 1. Preserved or Clean & dry 1A. Fluids in place: 3D map V3 URTeC Clean & dry 3. Connected porosity: 3D map 3A. Throat diameter: 3D map V2 (not presented) Numerical Simulations 4. OM vol. fraction: 3D map K upscaled to sub-plug 5. Ion-milled sub-plug BSEM: 2D mosaics SEM-EDS: 2D mineral maps 6. FIBSEM cubes K(φ, OM) pore-scale

12 From Nanopore to Seismic Scale - Mapping Porosity & OM (V1) X-ray contrast enhancement strategies for shales Dry or Cleaned Pore Saturated/Stained Diiodomethane (CH 2 I 2 ) [Difference] Porosity Organic Matter Iodine (I 2 ) TOC 3D-CTscans 3D-CTscans 3D-Calculated

13 From Nanopore to Seismic Scale - Mapping Porosity & OM (V1) DRY TOMOGRAM DIFFERENCE POROSITY [CH 2 I 2 -SATURATED DRY] DIFFERENCE TOC [I 2 -STAINED DRY] Bright: high porosity Bright: high TOC D D D C C C B B B A A A

14 From Nanopore to Seismic Scale - Mapping Porosity & OM (V1) DRY TOMOGRAM DIFFERENCE POROSITY [CH 2 I 2 -SATURATED DRY] DIFFERENCE TOC [I 2 -STAINED DRY] Bright: high porosity Bright: high TOC B: Organic Matter D D D B B B A: Fractures A A A

15 From Nanopore to Seismic Scale - Mapping Porosity & OM (V1) DRY TOMOGRAM DIFFERENCE POROSITY [CH 2 I 2 -SATURATED DRY] DIFFERENCE TOC [I 2 -STAINED DRY] D: Solid Calcite Fragment Bright: high porosity Bright: high TOC D D D C C C C: Organic Rich Foraminifer Chambers

16 From Nanopore to Seismic Scale - Mapping Porosity & OM (V1) DRY TOMOGRAM DIFFERENCE POROSITY [CH 2 I 2 -SATURATED DRY] DIFFERENCE TOC [I 2 -STAINED DRY] Bright: high porosity Bright: high TOC Sample Porosity (%) TOC (vol %) V Lab Data (wt%)

17 From Nanopore to Seismic Scale - Porosity TOC REV s (V1) DIFFERENCE POROSITY [CH 2 I 2 -SATURATED DRY] DIFFERENCE TOC [I 2 -STAINED DRY] 16.1% 8.1%

18 From Nanopore to Seismic Scale - mct / BSEM / EDS Overlay (V1) -Pampaenergfa ): FEI " DRY TOMOGRAM BSEM SEM-EDS FOV: 3.3 mm 1.9 mm Legend Mineral Area% Quartz (Silica) K-Na Feldspar Ca-Plagioclase 2.34 Biotite 0.26 Muscovite 0.76 Kaolinite 0.65 Illite 9.21 Chlorite 0.14 I/S Interstratified 6.43 Calcite (Aragonite) Apatite 0.79 Pyrite 2.14 Rutile 0.13 Unclassified / Organic 1.06

19 From Nanopore to Seismic Scale - mct / BSEM / EDS Overlay (V1) DRY TOMOGRAM BSEM SEM-EDS Dissolved foraminifera test filled with authigenic kaolinite book-like crystals infilled by OM, with an extensive network of expulsion cracks caused by fluid HC production. Pixel size: 10 nm FOV: 55 µm 140 µm Legend Mineral Area% Quartz (Silica) K-Na Feldspar Ca-Plagioclase 2.34 Biotite 0.26 Muscovite 0.76 Kaolinite 0.65 Illite 9.21 Chlorite 0.14 I/S Interstratified 6.43 Calcite (Aragonite) Apatite 0.79 Pyrite 2.14 Rutile 0.13 Unclassified / Organic 1.06

20 From Nanopore to Seismic Scale - mct / BSEM / EDS Overlay (V1) DRY TOMOGRAM BSEM SEM-EDS Dissolved foraminifera test filled with authigenic kaolinite book-like crystals infilled by OM, with an extensive network of expulsion cracks caused by fluid HC production. POROSITY MAP Bright: high porosity TOC MAP Bright: high TOC Pixel size: 10 nm FOV: 55 µm 140 µm

21 From Nanopore to Seismic Scale - FIBSEM (V1) pore space organic matter Sample Porosity (%) TOC (vol %) 20µm x 15.5µm x 5.5µm V1 FIBSEM V1 mct Lab Data (Wt%)

22 From Nanopore to Seismic Scale - Mapping Oil in Place (V3) UNCLEANED TOMOGRAM Black = No Oil DIFFERENCE [OIL-CONTRASTED DRY] Bright = Oil rich

23 From Nanopore to Seismic Scale - Mapping Oil in Place (V3) UNCLEANED TOMOGRAM OIL-CONTRASTED DRY TOC MAP C C C C C C C: Organic Rich Foraminifer Chambers FOV: 2.2 mm 1.6 mm Bright = Oil rich Black = No Oil Bright = OM rich Black = OM poor

24 From Nanopore to Seismic Scale - mct / BEM / EDS Overlay (V3) BSEM SEM-EDS FOV: 255 µm 173 µm Legend Mineral Ar Quartz (Silica) K-Na Feldspar Ca-Plagioclase Biotite Muscovite Kaolinite Illite Chlorite I/S Interstratified Calcite (Aragonite) Apatite Pyrite Rutile Unclassified / Organic 100% Solid 50/50 OIL-CONTRASTED DRY TOC MAP 100% OM

25 %TOC From Nanopore to Seismic Scale - DT/TOC correlation CAS.x-1 Multiwell (20 wells) Filtered DT curve (/10 samples) % TOC From lab. (cutting) DT_Filt10 %TOC= -9,56 + (0,16*DT_Filt10)

26 km From Nanopore to Seismic Scale - DT/TOC correlation APo.x EC-27 RDA.X-1001 COT_CORE

27 From Nanopore to Seismic Scale - TOC Cube Elastic Impedance Inversion QC: TOC Cube vs Well data Transit Time Cube workflow TOC Cube TOC-DT Function Neuronal network (Multiatributes)

28 From Nanopore to Seismic Scale - TOC Cube %TOC = (0,16*TT) - 9,6 Crossplot TOC Well vs TOC Neural TOC Well vs TOC Seismic TOC Cube: Lineal vs Neural TOC from well TOC from Seismic

29 From Nanopore to Seismic Scale - TOC Cube NE SW Qco RDA.e-2 RDA.x-1 C RDA.x-1001 B A3 VM A31 A2 A21 A1 A11 Tord - TOC (%) + 2 7

30 From Nanopore to Seismic Scale - TOC Cube 6,7 TOC (%) 2,0 6,7 RDA.e-2 RDA.x-1001 RDA.x-1 Cutoff = 4,3% TOC (%) A31 A21 A11 2,0

31 From Nanopore to Seismic Scale - TOC Cube Flattened to Vaca Muerta Fm. Top Horizon slice at top A11

32 From Nanopore to Seismic Scale - Sweet Spot by Secuence: A11 ( La Cocina ) Thickness (m) TOC (%) Young Thickness > 45 mts TOC > 5,5% Young > Overpressure (kg/cm2) Overlapping areas

33 From Nanopore to Seismic Scale - Conclusions Production Linked to high TOC zones 3D mapping of porosity and OM Porosity mainly associated with expulsion cracks in the OM 3D map of Oil in place Oil strongly associated with OM Upscaling Properties obtained from FIBSEM upscaled to sub-plug Driver upscaled from core/well to seismic: TOC Cube G&G Sweet Spot: Defining/Refining objectives based on OM distribution

34 From Nanopore to Seismic Scale - Conclusions -Pampa energia

35 Thanks to Pampa Energía y Total Austral for permiting publishing these results