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Rock Typing.pdf

This document discusses rock typing, which involves classifying reservoir rocks into unique units based on their depositional environment and subsequent diagenetic changes that result in distinct porosity-permeability relationships. The document outlines the rock typing methodology, which includes selecting key wells, identifying rock types in those wells, predicting rock types in uncored sections, and modeling rock type distribution between wells. The document also discusses common pitfalls in rock typing and provides an example case study where rock typing was used to investigate high water cuts during production.

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Ebrahim Heydari ConsultantPetrophysicist Ebrahim.Heydari@gmail.com ROCK TYPING Application in reservoir modelling and development October 15 2019
2 Outline • Geological characterization and modelling • Importance and objective of rock typing • Rock typing methodology • Rock type requirements • Pitfalls in rock typing • Rock typing schedule • Example of application of rock typing study ⎯ Lithostratigraphy and chronostratigraphyunits ⎯ Petrophysical/reservoir groups (Electrofacies, Winland ⎯ Rock type
• Primary objective of geological characterization and modelling is to predict the spatial variation of petrophysical and reservoir properties (any property that can be measured in terms of real numerical values) • Main inputs: ⎯ Project boundary and locations ⎯ Well data ⎯ Faults and fractures ⎯ Fluid contacts 3 Geological characterization and modelling ⎯ Horizons and tops (Zones) ⎯ Properties(petrophysicalgroups,clay content, porosity, permeability, N/Gand SHFs)
• Two forms of geological tops/zones are applied in the modeling • They constitute the basic elements of the reservoir architecture ⎯ Lithostratigraphic: Each zone shows a different lithology, fossil content. i.e formation ⎯ Chronostratigraphic: Each zone shows a different interval of geologic time. i.e parasequence 4
5 Reservoir properties (Petrophysical groups) • Units of rocks with similar petrophysical and reservoir quality (porosity, permeability, FZI, capillary pressure, pore throat size distribution, NMR T2, Winland R35, Pittman , Lucia, Leverette K/PHI and etc) A B C Swi Pressure or Height NPHI DT A C B Core Perm A Core Porosity . . .. . . . . . .. . . .. . . . . .. . .. C B Lithology, Porosity, Fluid Sat’n, Net Pay GR Sw C B A Porosity, Permeability, Pore size distribution Petrology, Mineralogy, Diagenesis,Porosity Electrofacies Saturation PTS Capillarypressure curves
6 Chronostratigraphic units vs petrophysical groups • GENERALLY NO LINK, typically chronostratigraphic units are anisotropic, spatially complex and internally heterogeneous. This is mainly because of change in lithology, grain size, sorting and texture Sw vs. height Sediment Grain PHI vs K Crevasse Splay Channel Fill Proximal Distributary Mouth Bar Distal Distributary Mouth Bar Prodelta Delta Front Delta Front Sandstone Delta Front Siltstone Pro-Delta Shales Offshore Clay Sea Level
7 Lithostratigraphic units vs petrophysical groups • GENERALLY NO LINK, sometimes link. Lithostratigraphic units could be heterogeneous. This is mainly because of change in grain size and sorting Porosity Permeability Medium to coarse sand, Mean size: 0.5 mm Very fine sand, Mean size: 0.1 mm Permeability Very well sorted Mod.-Well sorted Very poorly sorted Extremely poorly sorted Porosity Modified after Rabiller, 2006
8 Carbonate reservoirs • NO LINK, typically both units in carbonates are very heterogeneous. This is mainly because of diagenesis PHI K PHI K 1 PHI K 8
9 Definition of rock type • HIGHLIGHT: geological zones and petrophysical groups need to be integrated by a model, ROCK TYPING • Rock types: Units of rock deposited under similar conditions which experienced similar digenetic processes resulting in a unique porosity-permeability relationship, capillary pressure profile and water saturation for a given height above free water in a reservoir (Archie, 1950)
10 Rock typing objective • The ultimate objective of rock typing is to drive a framework for rock property distribution within 3D reservoir models that both honours the well data and predicts reservoir properties in uncored wells and between wells (Hollis et al., 2010) • The main Rock Typing study objective is to contribute to build dynamic model by an accurate 3D- distribution of geological facies being petrophysically meaningful through porosity, permeability, Pc and Kr. To achieve this point, petrophysical properties have to rely on 3D predictive variables i.e. geological facies (Rebelle et al., 2009)
11 Rock typing methodology • There are four main phases in each rock typing study 1- Selection of key wells
12 Rock typing methodology, Selection of key wells • Key wells should be selected from the wells with maximum data (preferably wells with cores, Image logs, NMR and pressure data) • They should cover reservoir laterally and vertically to capture heterogeneity OWC OWC
13 Rock typing methodology 1. Selecting key wells 2. Identification of rock types in key wells
14 Rock typing methodology • The ultimate Rock type identification should be done by integration of all available data, geological facies and SCAL data are the most important data pores ↔ “rooms” pore-throats ↔ “doors” Optimal set of data for rock typing, Alabi et al. (2014) and Chitale et al. (2015)
Rock typing methodology, Identification of rock types • Each rock type should honour followings (Varavur et al., 2005): ⎯ They must present unique reservoir behaviour ⎯ They should be predictablein un cored sections and wells ⎯ Eachrock type must be correlativeand mappablebetweenwells Methodology, 2: Identification of rock types 15
16 Rock typing methodology, Identification of rock types Copyright 2019 Baker Hughes CompanyLLC. All rightsreserved. • Never forget data QC, corrections and nature of data type, for example what is the permeability in 3D model? • Based on review of more than 50000 SCAL experiments more than 70% of them are unfit for purpose, (McPhee, 2015) ⎯ Air permeability? ⎯ Klinkenberg? Measured or from a correlation? ⎯ Brine permeability? ⎯ Ambient or stressed? ⎯ How measured? ⎯ How were plug prepared?
17 Rock typing methodology Copyright 2019 Baker Hughes CompanyLLC. All rightsreserved. 1. Selecting key wells 2. Identification of rock types in key wells 3. Prediction of rock types in un cored sections of wells and other un cored wells
18 Rock typing methodology, Prediction in un cored wells • The relationship between rock types and common data in key wells (normally logs) is discovered for each rock type and used to back calculate rock types Figures from help • If extensive core data are available this stage is less important
1. Selecting key wells 2. Identification of rock types in key wells 3. Prediction of rock types in un cored sections of key wells and other un cored wells 4. Rock type modeling (modelling between wells in 3D model) 19 Rock typing methodology
20 Rock typing methodology, Prediction between wells • Why prediction between wells is the most important part? ⎯ Reservoir is Arab-D, average thickness is 100 m ⎯ Vb = bulk rock volume of reservoir is 875,000,000,000 m3 (250000*35000*100) ⎯ There are more than 3400 wells in Ghawar field ⎯ Reservoir volume drilled by each well is 3.65 m3 ⎯ Reservoir volume drilled by 3400 wells is 12,441 m3 ⎯ Ratio of reservoir cut by wells is less than 0.000000014 Ghawar field
Rock typing methodology, Prediction between wells 21 • The spatial 3D distribution of the rock types should be predictable within the geological, geophysical or reservoir framework (between wells)
Rock typing methodology, Prediction between wells 22 • The spatial Clear image of shape, distribution, trend and size of facies/rock types is essential to propagate them between wells
Rock typing methodology, Prediction between wells 23 Class 3 poro-perm 3 SHF 3 Rel. perm. 3
Rock typing methodology, Prediction between wells 24 RT 3 K=100 MD SW=0.20 RT 1 RT 2 RT 3 RT 4 PHI=0.20 HEIGHT=250 ft K= 3 MD SW=0.30 K= 9 MD SW= 0.25 K= 100 MD SW=0.2 K= 2000 MD SW= 0.12
Rock typing methodology, Prediction between wells 25 RT 2: Reservoir RT 1: Tight A B D C 5 km RT2 RT1 RT2 RT2 RT1 RT1 RT1 RT2
Pitfalls in rock typing 26 • Rock typing without using SCAL data as the main input • Sedimentological maps are not integrated in the process • Sedimentological litofacies and diagenetic descriptions are not completed in a consistent approach, preferably by the same interpreter for all wells, example from Piper • Facies are described without diagenetic overprints • Sampling for SCAL is done before taking into account the distribution of the petrophysical groups. (each rock type needs a unique SCAL representative) • Poro-perm transforms are applied without linking to geological facies and stratigraphic position (Winland R35,Pitman and Lucia) • Using conventional data and methods in Carbonates • Using data without QC
Rock typing schedule 27 • Average thickness • and properties . 1- Exploration 3- Brown field • 3D seismic • Structural model with faults • Facies/RT modelling • Probabilistic models • SHF 2- Green field • 2D/3D seismic • Simple 3D grid • Deterministic model 4- Water flooding, EOR/EGR • 4D seismic • Structural model with all faults • Facies/RTmodelling 1 2 3 4 5 Simplemodel In depthmodel 5- Abandonment Data
Case study 28 • Structure: 15*7 km NE-SW anticline • Original hydrocarbon in place: 1500 MMSTB • Early water cut: 8% Mishrif Laffan Khatiyah
Case study, problem 29 • 7 exploration and delineation wells • Production started with four wells, 15 more producer and 10 injector wells were drilled in very short time • Shortly after start of production and water injection water cut significantly increased • This was resulted in having lower recover factor and reserve and higher residual oil • High water cut is a frequent problem that oil producers face at the late stage of oilfield development and production. The main reason of this problem is water breakthrough from the injection wells trough high permeable intervals and zones
Case study, rock typing 30 • A detailed rock typing was carried out to investigate the effect of reservoir quality and rock types on water flooding, residual oil and recovery factor • Six major rock types were classified in cored wells and subsequently predicted in un-cored wells
Case study, Residual oil saturation 31 • Residual oil saturation of each rock type was defined from wireline and cased hole logs in flooded intervals • All rock types except for rock type 1 show residual oil • Maximum residual oil was seen in rock type 6
Case study, Water flooding test 32 • The Two water flood experiments on composite cores of rock type 4 and 6 were used. These tests were carried out under reservoir conditions using oil and water similar to the actual reservoir fluids. Water injection was carried out at a constant rate of 10 cm3/h Compositecore Compositecoreof rock type4 Compositecoreof rock type6 Reservoir quality Porosity (%) 20 17 Air permeability (mD) 28 410 Water permeability (mD) 27 405 Permeability to reservoir oil at Swi (mD) 27 398 Irreducible water saturation (Swi) 13.6 17.3 Test Initial injection rate (cm3/h) 10 10 Front advance velocity (m/day) 0.34 0.81 Water Pore volume (PV) injected 31.1 15.3 breakthrough Cumulativeoil recovery (%IOIP) 27.4 16.2 End of water flood at 10 cm3/h Pore volume (PV) injected 537.7 432 Oil recovery (%IOIP) 53.4 21.7 Sor (%) 35.8 67.6 • Composite core of rock type 6 presents an earlier water breakthrough and higher Sor compared to the composite core of rock type 4 (similar observation in CPIs)
Case study, Pore throat size 33 • Rock type 6 presents a dual pore throat diameter distribution. It appears that only pores related to the larger mode of pore throat diameter distribution (larger than 5-10 micron) have been water flooded. In this rock type, water typically follows a preferential flow path through the high permeability zones and consequently some oil is by-passed Before water flooding After water flooding RT-4 RT-6 Oil Water
Case study, pore throat size 34 • In contrast, the narrow and sorted pore throat diameter distribution in rock type 4 connected all the pores with injected water and therefore strongly increased the oil recovery leading to less residual oil compared to rock type 6 RT-4 RT-6 • If only pores related to the larger mode of rock type 6 are flushed, oil recovery for this system would be less than 42 % IOIP
Conclusions 35 • Rock-typing is a synergetic workflow between the geologist, petrophysicist and reservoir engineer and the accurate determination and propagation of rock types requires a comprehensive integration of petrophysical, geological and reservoir data • No matter what software or prediction technique we apply to a variable, we are unlikely to achieve an acceptable result unless we take geological facts/effects into account
Summary of selected rock typing studies typing studies 37 Reference Formation/ Location Rock Type Definitions Data Sources / Evaluation Methodologies Davies, et al. [1991] Travis Peak sands, East Texas Salt Basin No specific definitions Depositional environments, sand body geometry, dimensions from core descriptions Texture, composition, lithology from microscopic imaging No quantitative porosity-permeability ranges; provided qualitative indicators Porras, et al. [1999] Santa Barbara and Pirital Field sands, Eastern Venezuela Basin Lithofacies, petrofacies Physical core descriptions of both large-scale & small-scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data Davies, et al. [1999] Wilmington Field Pliocene-Age sands No specific definitions Depositional environments, sand body geometry, dimensions from core descriptions Texture, composition, lithology from microscopic imaging No quantitative porosity-permeability ranges; provided qualitative indicators Boada, et al. [2001] Santa Rosa Field, Eastern Venezuela Basin Lithofacies, petrofacies Physical core descriptions of both large-scale & small-scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data Leal, et al. [2001] Block IX sands, Lake Maracaibo Basin, Venezuela Lithofacies, petrofacies Physical core descriptions of both large-scale & small-scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data
Summary of selected rock typing studieses 38 Reference Formation/Location Rock TypeDefinitions Data Sources / Evaluation Methodologies Madariage, et al. [2001] Sandstone/ C4 & C5 sands, Lagunillas Field, Lake Maracaibo Basin, Venezuela Lithofacies, petrofacies Physical core descriptions of both large-scale & small- scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability, electrical properties; dominant pore throat diameter from mercury-injection capillary pressure data Porras, et al. [2001] Tertiary & Cretaceous sands, Santa Barbara Field, Eastern Venezuela Basin No specific definitions Physical core descriptions of both large-scale & small- scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data Soto, et al. [2001] K1 sands, Suria No specific definitions Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data & Reforma- Libertad Fields, Used fuzzy logic to predict rock types in uncored wells Apiay-Ariari Basin, Columbia Marquez, et al. [2001] LL-04 sands, Tia Lithofacies, petrofacies Identification of stratigraphic units and lithofacies from log analysis Juana Field, Lake Maracaibo Basin, Venezuela Physical core descriptions of small-scale features; microscopic imaging of texture, composition, lithology Core-based measurement of porosity, permeability; up scaled permeability using NMR log measurements Ali-Nandalal & Gunter [2003] Pliestocene-age Geological facies, Petrophysical rock types Facies identified using core- and log-based analyses of primary sedimentary structures sands, Mahogany Field, Columbus Basin, Venezuela Core-based measurement of porosity, permeability and electrical properties
Summary of selected rock typing studies 39 Reference Formation/Location Rock Type Definitions Data Sources / Evaluation Methodologies Ohen, et al. [2004] Shushufindi Field sands, Oriente Basin, Ecuador Geological facies Facies identified using core- and log-based analyses of stratigraphy, structure, depositional environment Core-based measurement of porosity, permeability and water saturation Acosta, et al. [2005] El Furrial Field sands, Venezuela Geological facies, Petrophysical rock types Facies identified using core- and log-based analyses of stratigraphy, structure, depositional environment Core-based measurement of porosity, permeability and water saturation; pore characteristics from mercury- injection capillary pressure data Guo, et al. [2005] Napo formation sands, Oriente Basin, Ecuador Petrophysical rock type -based measurement of porosity, permeability and water saturation; pore characteristics from mercury- injection capillary pressure data Shenawi, et al. [2007] Formation unknown,location unknown Geological facies, Petrophysical rock types Facies identified using log-based definitions of shale content, lithology (density log) Core-based measurement of porosity, permeability and water saturation; pore characteristics from mercury- injection capillary pressure data Neo, et al. [1998]; Grotsch, et al. Lower Cretaceous-age Thamama I, II, II reservoirs, offshore Abu Dhabi Depositional Depositional environments, lithologies identified from core-based descriptions and log-based analysis [1998]; Marzouk, et al.. [1998]; Al-Aruri, et al. [1998] facies, Lithofacies, Petrophysical rock types Petrophysical properties including lithology, grain size & distribution from microscopic imaging; porosity, permeability determined from core analysis; pore structure & geometry determined from mercury-injection capillary pressure data Mathisen, et al. [2001] Upper & lower Clearfork formations, North Robertson Unit, Gaines Co., TX Lithofacies, Electrofacies Geological interpretations & log response to identify basic lithofacies Log-based descriptions based on similarity of log responses reflecting differences in mineralogy & lithofacies
Summary of selected rock typing studies 40 Reference Formation/Location Rock TypeDefinitions Data Sources / Evaluation Methodologies Lee, et al. [2002]; Perez, at al. [2003, 2005 C1a, C1b, C2a,, C2b, C3-C5 Lithofacies, Electrofacies Identified lithofacies using core-based descriptions & microscopic imaging zones, Northwest Extension carbonate buildup, Salt Creek Field, Kent Co., TX Identified electrofacies using log-based descriptions based on similarity of log responses reflecting differences in mineralogy & lithofacies Aplin, et al. [2002] Lower Cretaceous-age Lithofacies, Reservoir rock type Identified lithofacies using log-based similarities in micro- resistivity curves; calibrated to core-based descriptions Thamama Group, onshore U.A.E. Identified reservoir rock types from core-based physical descriptions, microscopic imaging; evaluated porosity, permeability from core measurements; determined pore structure & dimensions from mercury- capillary pressure data Al-Habshi, at al. [2003] Upper Zakum Formation, Field "B", onshore Abu Dhabi Lithofacies Identified lithofacies from geological interpretations, log and core analyses, and paragenetic sequence analysis Determined physical properties from core measurements; identified diagenesis from microscopic imaging; determined pore structure & dimensions from mercury-injection capillary pressure data Bieranvand [2003] Upper Cennomanian carbonate reservoir Lithofacies, reservoir rock types Identified lithofacies from geological interpretations and core-based physical descriptions Evaluated permeability & porosity from routine core analysis; quantified pore structure using mercury-injection capillary pressure data Al-Farisi, et al. [2004] Offshore Abu Dhabi carbonate Electrofacies Evaluated rock fabric & texture from core studies; measured permeability & porosity from routine core analysis, measured pore structure from mercury- injection capillary pressure data Up scaled core-based rock types to electrofacies using log response
Summary of selected rock typing studies 41 Reference Formation/ Location Rock Type Definitions Data Sources / Evaluation Methodologies Meyer, et al. [2004] K1-K4 intervals, Upper Khuff Formation, offshore Abu Dhabi Lithofacies, Static rock types Depositional environments, lithologies identified from geological interpretations, core-based descriptions and log-based analysis Identified static rock types based on pore types determined from microscopic imaging and mercury- injection capillary pressure data Varavur, et al. [2005] No information Lithofacies, Petrophysical rock types Identified lithofacies from geological interpretations and core-based physical descriptions; lithofacies defined based on depositional texture, grain-size, sorting, diagenesis Evaluated permeability & porosity from routine core analysis; quantified pore structure using microscopic imaging and mercury-injection capillary pressure data Rebelle, et al. [2005] Lower Kharaib Lithofacies, Petrophysical rock types Identified lithofacies from geological interpretations and core-based physical descriptions; lithofacies defined based on depositional texture, lithology, diagenesis formation, onshore Abu Dhabi Evaluated permeability & porosity from routine core analysis; quantified pore structure using mercury-injection capillary pressure data Frank, et al. [2005] Shuaiba formation, Al Shaheen Field, offshore Qatar No definitions Identified rock types using routine core measurements, microscopic imaging, mercury-injection capillary pressure data, NMR characteristics Up scaled rock types using NMR logs Creusen, et al. [2007] Natih formation, Oman Primary rock types, Rock Primary rock types identified on pore scale using microscopic imaging as well as routine porosity & permeability measurements type associations Rock type associations reflect collections of primary rock types, pore types and size distribution

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Rock Typing.pdf

  • 1.
  • 2.
    2 Outline • Geological characterizationand modelling • Importance and objective of rock typing • Rock typing methodology • Rock type requirements • Pitfalls in rock typing • Rock typing schedule • Example of application of rock typing study ⎯ Lithostratigraphy and chronostratigraphyunits ⎯ Petrophysical/reservoir groups (Electrofacies, Winland ⎯ Rock type
  • 3.
    • Primary objectiveof geological characterization and modelling is to predict the spatial variation of petrophysical and reservoir properties (any property that can be measured in terms of real numerical values) • Main inputs: ⎯ Project boundary and locations ⎯ Well data ⎯ Faults and fractures ⎯ Fluid contacts 3 Geological characterization and modelling ⎯ Horizons and tops (Zones) ⎯ Properties(petrophysicalgroups,clay content, porosity, permeability, N/Gand SHFs)
  • 4.
    • Two formsof geological tops/zones are applied in the modeling • They constitute the basic elements of the reservoir architecture ⎯ Lithostratigraphic: Each zone shows a different lithology, fossil content. i.e formation ⎯ Chronostratigraphic: Each zone shows a different interval of geologic time. i.e parasequence 4
  • 5.
    5 Reservoir properties (Petrophysicalgroups) • Units of rocks with similar petrophysical and reservoir quality (porosity, permeability, FZI, capillary pressure, pore throat size distribution, NMR T2, Winland R35, Pittman , Lucia, Leverette K/PHI and etc) A B C Swi Pressure or Height NPHI DT A C B Core Perm A Core Porosity . . .. . . . . . .. . . .. . . . . .. . .. C B Lithology, Porosity, Fluid Sat’n, Net Pay GR Sw C B A Porosity, Permeability, Pore size distribution Petrology, Mineralogy, Diagenesis,Porosity Electrofacies Saturation PTS Capillarypressure curves
  • 6.
    6 Chronostratigraphic units vspetrophysical groups • GENERALLY NO LINK, typically chronostratigraphic units are anisotropic, spatially complex and internally heterogeneous. This is mainly because of change in lithology, grain size, sorting and texture Sw vs. height Sediment Grain PHI vs K Crevasse Splay Channel Fill Proximal Distributary Mouth Bar Distal Distributary Mouth Bar Prodelta Delta Front Delta Front Sandstone Delta Front Siltstone Pro-Delta Shales Offshore Clay Sea Level
  • 7.
    7 Lithostratigraphic units vspetrophysical groups • GENERALLY NO LINK, sometimes link. Lithostratigraphic units could be heterogeneous. This is mainly because of change in grain size and sorting Porosity Permeability Medium to coarse sand, Mean size: 0.5 mm Very fine sand, Mean size: 0.1 mm Permeability Very well sorted Mod.-Well sorted Very poorly sorted Extremely poorly sorted Porosity Modified after Rabiller, 2006
  • 8.
    8 Carbonate reservoirs • NOLINK, typically both units in carbonates are very heterogeneous. This is mainly because of diagenesis PHI K PHI K 1 PHI K 8
  • 9.
    9 Definition of rocktype • HIGHLIGHT: geological zones and petrophysical groups need to be integrated by a model, ROCK TYPING • Rock types: Units of rock deposited under similar conditions which experienced similar digenetic processes resulting in a unique porosity-permeability relationship, capillary pressure profile and water saturation for a given height above free water in a reservoir (Archie, 1950)
  • 10.
    10 Rock typing objective •The ultimate objective of rock typing is to drive a framework for rock property distribution within 3D reservoir models that both honours the well data and predicts reservoir properties in uncored wells and between wells (Hollis et al., 2010) • The main Rock Typing study objective is to contribute to build dynamic model by an accurate 3D- distribution of geological facies being petrophysically meaningful through porosity, permeability, Pc and Kr. To achieve this point, petrophysical properties have to rely on 3D predictive variables i.e. geological facies (Rebelle et al., 2009)
  • 11.
    11 Rock typing methodology •There are four main phases in each rock typing study 1- Selection of key wells
  • 12.
    12 Rock typing methodology,Selection of key wells • Key wells should be selected from the wells with maximum data (preferably wells with cores, Image logs, NMR and pressure data) • They should cover reservoir laterally and vertically to capture heterogeneity OWC OWC
  • 13.
    13 Rock typing methodology 1.Selecting key wells 2. Identification of rock types in key wells
  • 14.
    14 Rock typing methodology •The ultimate Rock type identification should be done by integration of all available data, geological facies and SCAL data are the most important data pores ↔ “rooms” pore-throats ↔ “doors” Optimal set of data for rock typing, Alabi et al. (2014) and Chitale et al. (2015)
  • 15.
    Rock typing methodology,Identification of rock types • Each rock type should honour followings (Varavur et al., 2005): ⎯ They must present unique reservoir behaviour ⎯ They should be predictablein un cored sections and wells ⎯ Eachrock type must be correlativeand mappablebetweenwells Methodology, 2: Identification of rock types 15
  • 16.
    16 Rock typing methodology,Identification of rock types Copyright 2019 Baker Hughes CompanyLLC. All rightsreserved. • Never forget data QC, corrections and nature of data type, for example what is the permeability in 3D model? • Based on review of more than 50000 SCAL experiments more than 70% of them are unfit for purpose, (McPhee, 2015) ⎯ Air permeability? ⎯ Klinkenberg? Measured or from a correlation? ⎯ Brine permeability? ⎯ Ambient or stressed? ⎯ How measured? ⎯ How were plug prepared?
  • 17.
    17 Rock typing methodology Copyright2019 Baker Hughes CompanyLLC. All rightsreserved. 1. Selecting key wells 2. Identification of rock types in key wells 3. Prediction of rock types in un cored sections of wells and other un cored wells
  • 18.
    18 Rock typing methodology,Prediction in un cored wells • The relationship between rock types and common data in key wells (normally logs) is discovered for each rock type and used to back calculate rock types Figures from help • If extensive core data are available this stage is less important
  • 19.
    1. Selecting keywells 2. Identification of rock types in key wells 3. Prediction of rock types in un cored sections of key wells and other un cored wells 4. Rock type modeling (modelling between wells in 3D model) 19 Rock typing methodology
  • 20.
    20 Rock typing methodology,Prediction between wells • Why prediction between wells is the most important part? ⎯ Reservoir is Arab-D, average thickness is 100 m ⎯ Vb = bulk rock volume of reservoir is 875,000,000,000 m3 (250000*35000*100) ⎯ There are more than 3400 wells in Ghawar field ⎯ Reservoir volume drilled by each well is 3.65 m3 ⎯ Reservoir volume drilled by 3400 wells is 12,441 m3 ⎯ Ratio of reservoir cut by wells is less than 0.000000014 Ghawar field
  • 21.
    Rock typing methodology,Prediction between wells 21 • The spatial 3D distribution of the rock types should be predictable within the geological, geophysical or reservoir framework (between wells)
  • 22.
    Rock typing methodology,Prediction between wells 22 • The spatial Clear image of shape, distribution, trend and size of facies/rock types is essential to propagate them between wells
  • 23.
    Rock typing methodology,Prediction between wells 23 Class 3 poro-perm 3 SHF 3 Rel. perm. 3
  • 24.
    Rock typing methodology,Prediction between wells 24 RT 3 K=100 MD SW=0.20 RT 1 RT 2 RT 3 RT 4 PHI=0.20 HEIGHT=250 ft K= 3 MD SW=0.30 K= 9 MD SW= 0.25 K= 100 MD SW=0.2 K= 2000 MD SW= 0.12
  • 25.
    Rock typing methodology,Prediction between wells 25 RT 2: Reservoir RT 1: Tight A B D C 5 km RT2 RT1 RT2 RT2 RT1 RT1 RT1 RT2
  • 26.
    Pitfalls in rocktyping 26 • Rock typing without using SCAL data as the main input • Sedimentological maps are not integrated in the process • Sedimentological litofacies and diagenetic descriptions are not completed in a consistent approach, preferably by the same interpreter for all wells, example from Piper • Facies are described without diagenetic overprints • Sampling for SCAL is done before taking into account the distribution of the petrophysical groups. (each rock type needs a unique SCAL representative) • Poro-perm transforms are applied without linking to geological facies and stratigraphic position (Winland R35,Pitman and Lucia) • Using conventional data and methods in Carbonates • Using data without QC
  • 27.
    Rock typing schedule 27 •Average thickness • and properties . 1- Exploration 3- Brown field • 3D seismic • Structural model with faults • Facies/RT modelling • Probabilistic models • SHF 2- Green field • 2D/3D seismic • Simple 3D grid • Deterministic model 4- Water flooding, EOR/EGR • 4D seismic • Structural model with all faults • Facies/RTmodelling 1 2 3 4 5 Simplemodel In depthmodel 5- Abandonment Data
  • 28.
    Case study 28 • Structure:15*7 km NE-SW anticline • Original hydrocarbon in place: 1500 MMSTB • Early water cut: 8% Mishrif Laffan Khatiyah
  • 29.
    Case study, problem 29 •7 exploration and delineation wells • Production started with four wells, 15 more producer and 10 injector wells were drilled in very short time • Shortly after start of production and water injection water cut significantly increased • This was resulted in having lower recover factor and reserve and higher residual oil • High water cut is a frequent problem that oil producers face at the late stage of oilfield development and production. The main reason of this problem is water breakthrough from the injection wells trough high permeable intervals and zones
  • 30.
    Case study, rocktyping 30 • A detailed rock typing was carried out to investigate the effect of reservoir quality and rock types on water flooding, residual oil and recovery factor • Six major rock types were classified in cored wells and subsequently predicted in un-cored wells
  • 31.
    Case study, Residualoil saturation 31 • Residual oil saturation of each rock type was defined from wireline and cased hole logs in flooded intervals • All rock types except for rock type 1 show residual oil • Maximum residual oil was seen in rock type 6
  • 32.
    Case study, Waterflooding test 32 • The Two water flood experiments on composite cores of rock type 4 and 6 were used. These tests were carried out under reservoir conditions using oil and water similar to the actual reservoir fluids. Water injection was carried out at a constant rate of 10 cm3/h Compositecore Compositecoreof rock type4 Compositecoreof rock type6 Reservoir quality Porosity (%) 20 17 Air permeability (mD) 28 410 Water permeability (mD) 27 405 Permeability to reservoir oil at Swi (mD) 27 398 Irreducible water saturation (Swi) 13.6 17.3 Test Initial injection rate (cm3/h) 10 10 Front advance velocity (m/day) 0.34 0.81 Water Pore volume (PV) injected 31.1 15.3 breakthrough Cumulativeoil recovery (%IOIP) 27.4 16.2 End of water flood at 10 cm3/h Pore volume (PV) injected 537.7 432 Oil recovery (%IOIP) 53.4 21.7 Sor (%) 35.8 67.6 • Composite core of rock type 6 presents an earlier water breakthrough and higher Sor compared to the composite core of rock type 4 (similar observation in CPIs)
  • 33.
    Case study, Porethroat size 33 • Rock type 6 presents a dual pore throat diameter distribution. It appears that only pores related to the larger mode of pore throat diameter distribution (larger than 5-10 micron) have been water flooded. In this rock type, water typically follows a preferential flow path through the high permeability zones and consequently some oil is by-passed Before water flooding After water flooding RT-4 RT-6 Oil Water
  • 34.
    Case study, porethroat size 34 • In contrast, the narrow and sorted pore throat diameter distribution in rock type 4 connected all the pores with injected water and therefore strongly increased the oil recovery leading to less residual oil compared to rock type 6 RT-4 RT-6 • If only pores related to the larger mode of rock type 6 are flushed, oil recovery for this system would be less than 42 % IOIP
  • 35.
    Conclusions 35 • Rock-typing isa synergetic workflow between the geologist, petrophysicist and reservoir engineer and the accurate determination and propagation of rock types requires a comprehensive integration of petrophysical, geological and reservoir data • No matter what software or prediction technique we apply to a variable, we are unlikely to achieve an acceptable result unless we take geological facts/effects into account
  • 37.
    Summary of selectedrock typing studies typing studies 37 Reference Formation/ Location Rock Type Definitions Data Sources / Evaluation Methodologies Davies, et al. [1991] Travis Peak sands, East Texas Salt Basin No specific definitions Depositional environments, sand body geometry, dimensions from core descriptions Texture, composition, lithology from microscopic imaging No quantitative porosity-permeability ranges; provided qualitative indicators Porras, et al. [1999] Santa Barbara and Pirital Field sands, Eastern Venezuela Basin Lithofacies, petrofacies Physical core descriptions of both large-scale & small-scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data Davies, et al. [1999] Wilmington Field Pliocene-Age sands No specific definitions Depositional environments, sand body geometry, dimensions from core descriptions Texture, composition, lithology from microscopic imaging No quantitative porosity-permeability ranges; provided qualitative indicators Boada, et al. [2001] Santa Rosa Field, Eastern Venezuela Basin Lithofacies, petrofacies Physical core descriptions of both large-scale & small-scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data Leal, et al. [2001] Block IX sands, Lake Maracaibo Basin, Venezuela Lithofacies, petrofacies Physical core descriptions of both large-scale & small-scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data
  • 38.
    Summary of selectedrock typing studieses 38 Reference Formation/Location Rock TypeDefinitions Data Sources / Evaluation Methodologies Madariage, et al. [2001] Sandstone/ C4 & C5 sands, Lagunillas Field, Lake Maracaibo Basin, Venezuela Lithofacies, petrofacies Physical core descriptions of both large-scale & small- scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability, electrical properties; dominant pore throat diameter from mercury-injection capillary pressure data Porras, et al. [2001] Tertiary & Cretaceous sands, Santa Barbara Field, Eastern Venezuela Basin No specific definitions Physical core descriptions of both large-scale & small- scale features; microscopic imaging of texture, composition, lithology, diagenesis Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data Soto, et al. [2001] K1 sands, Suria No specific definitions Core-based measurement of porosity, permeability; dominant pore throat diameter from mercury-injection capillary pressure data & Reforma- Libertad Fields, Used fuzzy logic to predict rock types in uncored wells Apiay-Ariari Basin, Columbia Marquez, et al. [2001] LL-04 sands, Tia Lithofacies, petrofacies Identification of stratigraphic units and lithofacies from log analysis Juana Field, Lake Maracaibo Basin, Venezuela Physical core descriptions of small-scale features; microscopic imaging of texture, composition, lithology Core-based measurement of porosity, permeability; up scaled permeability using NMR log measurements Ali-Nandalal & Gunter [2003] Pliestocene-age Geological facies, Petrophysical rock types Facies identified using core- and log-based analyses of primary sedimentary structures sands, Mahogany Field, Columbus Basin, Venezuela Core-based measurement of porosity, permeability and electrical properties
  • 39.
    Summary of selectedrock typing studies 39 Reference Formation/Location Rock Type Definitions Data Sources / Evaluation Methodologies Ohen, et al. [2004] Shushufindi Field sands, Oriente Basin, Ecuador Geological facies Facies identified using core- and log-based analyses of stratigraphy, structure, depositional environment Core-based measurement of porosity, permeability and water saturation Acosta, et al. [2005] El Furrial Field sands, Venezuela Geological facies, Petrophysical rock types Facies identified using core- and log-based analyses of stratigraphy, structure, depositional environment Core-based measurement of porosity, permeability and water saturation; pore characteristics from mercury- injection capillary pressure data Guo, et al. [2005] Napo formation sands, Oriente Basin, Ecuador Petrophysical rock type -based measurement of porosity, permeability and water saturation; pore characteristics from mercury- injection capillary pressure data Shenawi, et al. [2007] Formation unknown,location unknown Geological facies, Petrophysical rock types Facies identified using log-based definitions of shale content, lithology (density log) Core-based measurement of porosity, permeability and water saturation; pore characteristics from mercury- injection capillary pressure data Neo, et al. [1998]; Grotsch, et al. Lower Cretaceous-age Thamama I, II, II reservoirs, offshore Abu Dhabi Depositional Depositional environments, lithologies identified from core-based descriptions and log-based analysis [1998]; Marzouk, et al.. [1998]; Al-Aruri, et al. [1998] facies, Lithofacies, Petrophysical rock types Petrophysical properties including lithology, grain size & distribution from microscopic imaging; porosity, permeability determined from core analysis; pore structure & geometry determined from mercury-injection capillary pressure data Mathisen, et al. [2001] Upper & lower Clearfork formations, North Robertson Unit, Gaines Co., TX Lithofacies, Electrofacies Geological interpretations & log response to identify basic lithofacies Log-based descriptions based on similarity of log responses reflecting differences in mineralogy & lithofacies
  • 40.
    Summary of selectedrock typing studies 40 Reference Formation/Location Rock TypeDefinitions Data Sources / Evaluation Methodologies Lee, et al. [2002]; Perez, at al. [2003, 2005 C1a, C1b, C2a,, C2b, C3-C5 Lithofacies, Electrofacies Identified lithofacies using core-based descriptions & microscopic imaging zones, Northwest Extension carbonate buildup, Salt Creek Field, Kent Co., TX Identified electrofacies using log-based descriptions based on similarity of log responses reflecting differences in mineralogy & lithofacies Aplin, et al. [2002] Lower Cretaceous-age Lithofacies, Reservoir rock type Identified lithofacies using log-based similarities in micro- resistivity curves; calibrated to core-based descriptions Thamama Group, onshore U.A.E. Identified reservoir rock types from core-based physical descriptions, microscopic imaging; evaluated porosity, permeability from core measurements; determined pore structure & dimensions from mercury- capillary pressure data Al-Habshi, at al. [2003] Upper Zakum Formation, Field "B", onshore Abu Dhabi Lithofacies Identified lithofacies from geological interpretations, log and core analyses, and paragenetic sequence analysis Determined physical properties from core measurements; identified diagenesis from microscopic imaging; determined pore structure & dimensions from mercury-injection capillary pressure data Bieranvand [2003] Upper Cennomanian carbonate reservoir Lithofacies, reservoir rock types Identified lithofacies from geological interpretations and core-based physical descriptions Evaluated permeability & porosity from routine core analysis; quantified pore structure using mercury-injection capillary pressure data Al-Farisi, et al. [2004] Offshore Abu Dhabi carbonate Electrofacies Evaluated rock fabric & texture from core studies; measured permeability & porosity from routine core analysis, measured pore structure from mercury- injection capillary pressure data Up scaled core-based rock types to electrofacies using log response
  • 41.
    Summary of selectedrock typing studies 41 Reference Formation/ Location Rock Type Definitions Data Sources / Evaluation Methodologies Meyer, et al. [2004] K1-K4 intervals, Upper Khuff Formation, offshore Abu Dhabi Lithofacies, Static rock types Depositional environments, lithologies identified from geological interpretations, core-based descriptions and log-based analysis Identified static rock types based on pore types determined from microscopic imaging and mercury- injection capillary pressure data Varavur, et al. [2005] No information Lithofacies, Petrophysical rock types Identified lithofacies from geological interpretations and core-based physical descriptions; lithofacies defined based on depositional texture, grain-size, sorting, diagenesis Evaluated permeability & porosity from routine core analysis; quantified pore structure using microscopic imaging and mercury-injection capillary pressure data Rebelle, et al. [2005] Lower Kharaib Lithofacies, Petrophysical rock types Identified lithofacies from geological interpretations and core-based physical descriptions; lithofacies defined based on depositional texture, lithology, diagenesis formation, onshore Abu Dhabi Evaluated permeability & porosity from routine core analysis; quantified pore structure using mercury-injection capillary pressure data Frank, et al. [2005] Shuaiba formation, Al Shaheen Field, offshore Qatar No definitions Identified rock types using routine core measurements, microscopic imaging, mercury-injection capillary pressure data, NMR characteristics Up scaled rock types using NMR logs Creusen, et al. [2007] Natih formation, Oman Primary rock types, Rock Primary rock types identified on pore scale using microscopic imaging as well as routine porosity & permeability measurements type associations Rock type associations reflect collections of primary rock types, pore types and size distribution