Source Rocks and Petroleum System Modeling

Petroleum systems modeling for highly-accurate prediction of undiscovered hydrocarbon accumulations. Case study: 3D PS model of Southern border of Dnieper-Donets basin.

I. Software and workflow II. Accuracy of prediction Case study: 3D Petroleum system model of Southern border of Dnieper-Donets basin

a. Objectives, methods, inputs b. Identification and characterization of petroleum source rocks c. Determination of paleo-intensity of heatflow and 1D modeling for calibration d. 2D modeling of regional cross-sections e. Identification of petroleum system elements f. Petrophysical properties of reservoirs and seals g. Development of 3D geological model and PetroMod simulations



Modeling software “Basin modeling was for many years considered as “a niche discipline”, mainly propagated and used by geochemists. What a fundamental error and misunderstanding! Add pic with flow path The absolute contrary is the truth.” Dietrich Welte, Fundamentals of Basin and Petroleum Systems Modeling, 2009

Schlumberger’ software PetroMod™ is a universal modern tool for Petroleum Systems Modeling Petroleum systems models is large-scale 3D geologic models, which cover areas ranging from a single prospect area to regional studies of entire basins to resource assessments of mega-regional scale covering multiple basins. The models provide a complete record of the evolution of a petroleum system, including pressure and temperature history. They relate the structural evolution of a basin to generation, migration, accumulation, and loss of oil and gas in a petroleum system through geologic time. Properties such as gas/oil ratios and API gravities can be analyzed, understood, and predicted. A petroleum systems model provides the only means to integrate all physical aspects (source, trap, seal, and reservoir) and time (charge) to quantify and analyze processes and reduce exploration risk.

PetroMod™ developed by

It combines several decades of research results by the best researchers fulfilled in world class institutions:

3D model of Eastern Poltava region, Ukraine. Southern border zone of Dnieper-Donets basin

Workflow of Basin Analysis and Petroleum Systems Modeling via PetroMod™ “ I believe much of future exploration success will come from the marriage of seismic and petroleum systems modeling software.”

362 mln yr

Structural geology

Stratigraphy

Geological modeling

333 mln yr

322 mln yr

310 mln yr

Oil & gas generation, migration and accumulation

Today

3D model of Eastern Poltava region, Ukraine. Southern border zone of Dnieper-Donets basin

Modeling of geological evolution

In essence, basin and petroleum system modeling (BPSM) tracks the evolution of a basin through time as it fills with fluids and sediments that may eventually generate or contain hydrocarbons (left). In concept, BPSM is analogous to a reservoir simulation, but with important differences. Reservoir simulators model fluid flow during petroleum drainage to predict production and provide information for its optimization. The distance scale is meters to kilometers, and the time scale is months to years. Although the flow is dynamic, the model geometry is static, remaining unchanged during the simulation. Basin and petroleum system modeling brings together several dynamic processes, including sediment deposition, faulting, burial, kerogen maturation kinetics and multiphase fluid flow. These processes may be examined at several levels, and complexity typically increases with spatial dimensionality; the simplest, 1D modeling, examines burial history at a point location. Two-dimensional modeling, either in map or cross section, can be used to reconstruct oil and gas generation, migration and accumulation along a cross section. Three-dimensional modeling reconstructs petroleum systems at reservoir and basin scales and has the ability to display the output in 1D, 2D or 3D, and through time.

Time scale

John Dolson, Understanding Oil and Gas Shows and Seals in the Search for Hydrocarbons, 2016



Forecast of oil and gas fields within Southern border area of Dnieper-Donets basin 1 – Malosorochyns’ke oilfield 2 – Radchenkivs’ke oilfield 3 – Kybyntsivs’ke oilfield 4 – Velikobagachans’ke gas field 5 - Sagaidaks’ke oilfield, 6 – Shkurupiyvs’ke field 7 – Machukha gas field 8 - West Vylshyans’ke oilfield 9.1 - Reshetnyakivs’ke oilfield 9.2 – West Reshetnyakivs’ke gas field 10 – Lymans’ke gas field 11 – Zachepylivs’ke oilfield 12 – Sukhodolivs’ke gas field 13 - Novogrygoryvs’ke oilfield 14 - Mochanivs’ke oilfield 15 – Gnativs’ke oilfield 16 – Lyvens’ke gas field 17 – Mykhailivs’ke gas field 18 – Dmukhailivs’ke gas field 19 - Kremenivs’ke oil&gas field 20 – Lychkivs’ke oil field 21 – Novoselivs’ke field 22 - East Novoselivs’ke 23 –Proletars’ke 0,7 bcm 24 - Ul’yanivs’ke gas field 25 – Golubivs’ke field 26 - West Golubivs’ke field 27 – Pereschepyns’ke 28 – Bogatoivs’ke gas field 29 - Leventsivs’ke gas field

1 2

3

4 5 6

Poltava city 7

8 10

12

9.1 9.2

11 13 14 15

Proved fields - 29 False forecast – 5 Undiscovered - 9

16 17

18

19

20

23

Fields forecast accuracy – 85% Accuracy of fluid saturation - 82%.

27

22

21

28 24

30 km

25

26 29

Accuracy of predicted oil and gas fields is 85%. Accuracy of predicted fluid saturation is 82%. 3D petroleum system model of Eastern Poltava region, Ukraine. Southern border zone of Dnieper-Donets basin

Prediction of oil and gas accumulations in Upper Serpukhovian section (Carboniferous)

1 – Malosorochyns’ke oilfield 4,8 MMbbl 2 – Radchenkivs’ke oilfield, 1,6 MMbbl, 2,4 bcm 3 – Kibintsivs’ke oilfield, 0,294 MMbbl 4 – Sagaidaks’ke oilfield, 3,9 MMbbl 5 – West Vylshyans’ke oilfield 6 – Lymans’ke gas field 7 – Zachepylivs’ke oilfield 2,3 MMbbl 8 – Sukhodolivs’ke gas field 1,8 bcm 9 – Kremenivs’ke gas field 10 – Leventsivs’ke gas field 11 - Reshetnyakivs’ke oilfield 4,3 MMbbl

1

3

2

4 Poltava city

11

8

5 6

Proved accumulations - 11 False forecast – 9 Undiscovered - 5

7

9

Accumulations forecast accuracy – 55% Accuracy of fluid saturation - 72%.

10

30 km

Accuracy of prediction of oil and gas accumulations is 55%. Accuracy of oil or gas content prediction is 72%. 3D petroleum system model of Eastern Poltava region, Ukraine. Southern border zone of Dnieper-Donets basin

Prediction of oil and gas accumulations in Upper Visean section (Lower Carboniferous)

1 1 – Malosorochyns’ke oilfield 4,8 MMbbl 2 – Sagaidaks’ke oilfield, 3,9 MMbbl 3 – Shkurupiyvs’ke field 4 – Lymans’ke gas field 5 – Zachepylivs’ke field 2,3 MMbbl 6 – Stepne gas field 2 bcm 7 – Novogrygoryvs’ke oilfield 10 Mmbbl 8 - Mykhailivs’ke gas field 1bcm 9 - Yurivs’ke gas field 1bcm 10 - Kremenivs’ke gas field 5bcm 11 – Pereschepyns’ke 0,7 bcm 12 – East Novoselivs’ke 2 bcm 13 – Proletars’ke 2 bcm 14 – Golubivs’ke field 15 - East Golubivs’ke field

2 3 Poltava city

4 5


6

8

7

11 9

13

10 12

14 15

Accumulations forecast accuracy – 68% Accuracy of fluid saturation - 94%. 30 km

Accuracy of prediction of oil and gas accumulations is 68%. Accuracy of oil-to-gas prediction in predicted accumulations is 94%. 3D petroleum system model of Eastern Poltava region, Ukraine. Southern border zone of Dnieper-Donets basin

Prediction of oil and gas accumulations in Tournaisian carbonate play (Lower Carboniferous) 1 – Sagaidaks’ke oilfield 2 – Machukhs’ke gas field 3 – Lymans’he field 4 - Zachepylivs’ke field 1 bcm 5 - Mochanivs’ke oilfield 2,2 MMbbl, 5bcm 6 – Gnativs’ke oilfield 19 MMbbl, 5bcm 7 - Lyvens’ke gas field 8 - Yurivs’ke gas field 2bcm 9 – Leventsivs’ke gas field

1

Poltava city

2

3


4 5

Accumulations forecast accuracy – 56% Accuracy of fluid saturation - 66%.

6

7 9

9

Accuracy of prediction of oil and gas accumulations is 59%. Accuracy of oil-to-gas prediction in predicted accumulations is 66%. 3D petroleum system model of Eastern Poltava region, Ukraine. Southern border zone of Dnieper-Donets basin



Objectives, methods and input data

Case study: 3D model of Petroleum System of Southern border of Dnieper-Donets basin Objectives -Predict new hydrocarbon accumulations within study area - Obtain new knowledge about hydrocarbons generation, migration and accumulation in DDB - Obtain new knowledge for understanding the geochronology of evolution of petroleum system in DDB Methods - Identification and characterization of petroleum source rocks - Systematization and analysis of all known commercial HC accumulations and oil and gas shows - 1D modeling and calibration of 1D models according to vitrinite reflectance values - 2D modeling using regional seismic profiles - Development of 3D model - Calibration of 2D and 3D models according to the known oil and gas deposits within study area Inputs 2000 micro-images of 240 thin sections and polished sections of organic-rich rocks and coals, geochemical data obtained during previous studies by various research groups, petrophysical data of 2640 samples of reservoir rocks, interpretation of well logging data of about 80 boreholes, vitrinite reflectance values from 47 boreholes, well logs analysis for more accurate stratigraphic boundaries for 180 wells and identify lithological data, local and regional structural maps, local and regional seismic cross sections, all available data about commercial deposits and documented oil and gas shows.

Study area within Dnieper-Donets basin

3D model coverage

Area of source rocks study

Identification and characterization of petroleum source rocks along the Southern border of DDB

Identification and characterization of petroleum source rocks within Southern part of DDB Samples of Bashkirian age.

2000 micro-images of 240 samples for

identification of macerals and OM type DDB

USGS

USGS Samples of Lower Serpukhovian age.

DDB USGS

DDB Used classifications:

- Sýkorová et al. , 2005. - Classification of huminite – ICCP System 1994. - International Handbook of Coal Petrography, 1975 - Photomicrograph Atlas Organic Petrology, 1994

Identification and characterization of petroleum source rocks within Southern part of DDB Litho-facial analysis and correlations at sub-regional scale has identified numerous organic-rich formations with regional and semi-regional lateral distribution.

Identification and characterization of petroleum source rocks within Southern part of DDB EXSUDATINITE – liptinite maceral and source of oil generation from Carboniferous coal beds GREENLAND USGS USGS

DDB GREENLAND

USGS

GREENLAND

GREENLAND

USGS

DDB

DDB DDB

Exudatinite was identified in horizons C27, C31, б-1-2, б-6, б-8, с-7-8, с-9, с-10, с-11, в-26-27, т-1-4 With the highest concentration in horizons в-14-16

Properties and potential previously studied and described by authors:

Wan Hasiah, Abdullah, 1999 WOLFGANG KALKREUTH, 2012; Teichmüller, M. 1974; Kalkreuth, W. & Macauley, G. 1987; Littke Ralf, 1993;

Identification and characterization of petroleum source rocks within Southern part of DDB Porous funginite is filled with exudatinite and resinite which generates bitumen and oil at Ro = 1,0-1,1%.

Polished section blurred by oil

Ro=1,17%

400 µm

Evaluation of TOC content (total organic carbon, %) and identification of lateral distribution of organic-rich formations

Evaluation of organic-richness based on well logs using best practices: - Karpenko O.M. et al, 2014 - Passey Q.R. et al., 1991

Geochemical properties of organic-rich rocks and coals Kerogen types

We used data obtained during geochemical studies and shale gas studies by various research groups:  Ogar V.V., 2012  Arseniy Y.A. et al., 1984  Pryvalov V.A., 2012  Khomenko V.A., 1986  Machulina et al., 1993, ’95, 2001, ’04  Ivanov A.V., 2012  Mykhailov V.A. et al., 2014a  Lukin O.Yu., 2010, 2013, 2014  Karpenko I.O., 2015  Ulmishek et al., 1994, 2001  Vakarchuk S.G., 2015a & 2015b  Kabyshev B.P et al., 1999  Misch et al., 2015  Sachsenhofer R.F. et al., 2010  Misch et al., 2016 a & 2016 b  Radziwill A.Yu. et al. , 2012 Maps: sampling rocks and hydrocarbons and geochemical correlation between them ( δ13С )

Stratigraphic distribution of TOC and HI

Identification of OM type and generating potential

Results of comprehensive study of petroleum source rocks within Southern part of DDB Stratigraphic distribution of Carboniferous organic-rich formations and OM types* along the Southern border of DDB

*

* OM type: 1st number – major, 2nd – minor content

Defined stratigraphic distribution of organic-rich rocks and coals and identified the types of OM within study areas. Briefly findings:  Upper and Middle Carboniferous and Serpukhovian sediments concentrate all organic matter in thin coal beds (0,5-2,0mm) and coal horizons (1-3m) with dominated type III OM;  Serpukhovian age contains coal beds with significant concentration of liptinite (type II OM), horizons c-9-10-11 and c-18-C-19 formed by vitrinite maceral with a significant concentration of semifuzinite and funginite particles filled by exudatinite, small liptinite particles and liptodetrite (II-III-IV types OM). According to Sachsenhofer (2010) carbonate horizons c-1-3 and c-22-23 contains oil-prone OM (II type);  Upper Visean age contains av. 1.5-2.5% TOC with dominated humic OM (III type OM), TOC concentration increases up to v-22 horizon which contains 1.0-4.2% TOC (III type OM);  Horizons v-14-16 contain high concentration of large particles (up to 2 mm) of inertinite macerals which are highly porous and filled with maceral of liptinite - exudatinite and resinite (type II OM);  Rudov Beds (v-23) contains up to 10% TOC (II-III type OM) and has very limited distribution within study area and thickness up to of 3-8 meters;  Tournaisian age - horizons t-3-5 includes 2 layers with TOC up to 10% and marine oil prone (type II), but layer’s thickness is limited to 5-8 meters. The same horizons were correlated with 50-70 meter thininterbedded organic-rich carbonate horizons. According to Vakarchuk SG, 2015 – OM I-II and II-III types;  Lower-Visean horizons v-25-26 in NW part of DDB contain significant amount of sapropelic coal beds with II-III kerogen type;  Above-salt Devonian section contains coal layers (type III OM);  Inter-salt Devonian section - highly bituminous rocks with type II OM;  Under-salt Devonian section contains bituminous organic-rich formation with TOC up to 3-4% and another formation with 1,5-2,5% of TOC.

Petroleum system modeling and prediction of oil and gas potential in the Lower Carboniferous complex within Southern part of DDB.

Thermal maturity maps of top Devonian surface (A and B) and a map of oil generation rate during post-Permian time (C)

А

B

Boreholes used for 1D modeling marked on the map beneath

C

One of 1D models: geochronology of sedimentation, hydrocarbons generation rate, paleo-heatflow, etc. Deep samples from borehole East-Poltava 12

Geochronology of HeatFlow (mW/m2)

For 1D and 3D simulations kinetic algorithms were used "kerogen-HC" developed by Sweeney & Burnham, 1990

Determination of paleo-intensity of heatflow and 1D calibration according to vitrinite reflection values Power of paleo-heatflow according to results of Austrian research group (D. Misch et al., 2015)

1D modeling of borehole Bogatois’ka 25

Power of paleo-heatflow according to our results Borehole East-Poltava 12

Ro,%

Calibration 1D models according to vitrinite reflectance values (Ro,%)

2D model of regional cross-sections: inputs Basin evolution and thermal maturity at rift and post rift stages

Lithofacial data

PSE elements

Thermal conductivity

2D simulations: predicted accumulations and hydrocarbons saturation Hydrocarbons saturation

Predicted undiscovered sub-salt oil accumulations

2D simulations: predicted accumulations and hydrocarbons saturation Hydrocarbons saturation Predicted undiscovered sub-salt oil accumulations

Defining elements of petroleum systems in Serpukhovian section Stratigraphic distribution of commercial hydrocarbon accumulations and source rocks Within the Southern part of DDB two major levels of HC accumulation were identified: Upper Serpukhovian and Tournaisian.

* Accumulation

Sources

*

3D modeling of petroleum system and prediction of HC accumulation in Serpukhovian horizons C-1-C-6 were fulfilled based on structural features of the area, petrophysical properties of reservoir rocks, thermo-baric and geochemical characteristics, the results of 1D simulation and studied organic-rich horizons and comprehensive analysis. Upper Serpukhovian oil - Density of degassed oil (surface) 844 g/cm3, 36,15 API - Density of formation oil 666-842 g/cm3; 80-36,55 API - Viscosity of formation oil 3.25-3,56 mPa·s - Viscosity of degassed oil (surface) 7,826 mPa·s

* Two major levels of commercial HC accumulation - Upper Serpukhov and Tournaisian

Defining elements of petroleum systems in Lower Visean and Tournaisian sections Stratigraphic distribution of commercial hydrocarbon accumulations and source rocks Within the Southern part of DDB two major levels of HC accumulation were identified: Upper Serpukhovian and Tournaisian.

*

HC accumulation

HC Sources

*

Source rocks. The results of 3D modeling determined that Tournaisian and Upper Devonian productive horizons beneath the Visean carbonate seal were charged by Tournaisian oilprone carbonate source rocks and by hydrocarbon gases generated from coal beds in inter-salt Devonian section.

3D modeling of petroleum system and prediction of HC accumulation in Serpukhovian horizons C-1-C-6 were fulfilled based on structural features of the area, petrophysical properties of reservoir rocks, thermo-baric and geochemical characteristics, the results of 1D simulation and studied organic-rich horizons and comprehensive analysis. Tournaisian oil - Density of degassed oil (surface) 818,7-878,0 g/cm3 41,3-29,66 API - Density of formation oil 663,7-788,0 g/cm3 81,69-48 API - Viscosity of formation oil 0,26 mPa·s - Viscosity of degassed oil (surface) 6,41 mPa·s

* Two major levels of commercial HC accumulation - Upper Serpukhov and Tournaisian

Inputs for 3D modeling: Petrophysical properties of reservoirs and seals Comprehensive analysis of petrophysical properties of reservoir rocks (2640 samples) from borholes located within Southern and paraxial parts of DDB. Diagrams show compaction trend - relationship between total porosity and depth of the reservoir rocks (A, B), and average limit values of effective porosity and effective permeability (D). Also diagram C shows accepted relationship between effective porosity and capillary pressure of clay seal rocks with different clay-sand ratios and carbonate seal of Visean and Tournaisian represented by marls and limestones.

For sandstone reservoirs deeper 5,0 km

C D

3D modeling of HC migration and accumulation in the Upper Serpukhovian horizons C-1-C-6 along the Southern border of DDB

3D modeling of HC migration and accumulation in the Upper Serpukhovian horizons C-1-C-6 along the Southern border of DDB

3D modeling of HC migration and accumulation in the Lower Visean and Tournaisian complex along the Southern border of DDB

Cross section with forecasted oil and gas accumulations Results of 3D simulations

Results of 3D simulations: predicted hydrocarbon accumulations

Source Rocks and Petroleum System Modeling

Source Rocks and Petroleum System Modeling

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