FROM OBC SEISMIC TO POROSITY VOLUME: A PRE-STACK ANALYSIS OF A TURBIDITE RESERVOIR, DEEPWATER CAMPOS BASIN, BRAZIL.

Transcription

1 FROM OBC SEISMIC TO POROSITY VOLUME: A PRE-STACK ANALYSIS OF A TURBIDITE RESERVOIR, DEEPWATER CAMPOS BASIN, BRAZIL. by Luiz M. R. Martins

2 c Copyright by Luiz M. R. Martins, 2013 All Rights Reserved

3 A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Master of Science (Geophysics). Golden, Colorado Date Signed: Luiz M. R. Martins Signed: Dr. Thomas L. Davis Thesis Advisor Golden, Colorado Date Signed: Dr. Terence K. Young Professor and Head Department of Geophysics ii

4 ABSTRACT The Campos Basin is the best known and most productive of the Brazilian coastal basins. Turbidites are, by far, the main oil-bearing reservoirs. Using a four component (4-C) oceanbottom-cable (OBC) seismic survey I set out to improve the reservoir characterization in a deep-water turbidite field in the Campos Basin. In order to achieve my goal, pre-stack angle gathers were derived and PP and PS inversion were performed. The inversion was used as an input to predict the petrophysical properties of the reservoir. Converting seismic reflection amplitudes into impedance profiles not only maximizes vertical resolution but also minimizes tuning effects. Mapping the porosity is extremely important in the development of a hydrocarbon reservoirs. Combining seismic attributes derived from the P-P data and porosity logs I use linear multi-regression and neural network geostatistical tools to predict porosity between the seismic attributes and porosity logs at the well locations. After predicting porosity in well locations, those relationships were applied to the seismic attributes to generate a 3-D porosity volume. The predicted porosity volume highlighted the best reservoir facies in the reservoir. The integration of elastic impedance, shear impedance and porosity improved the reservoir characterization. iii

5 TABLE OF CONTENTS ABSTRACT iii LIST OF FIGURES vii LIST OF SYMBOLS xiv ACKNOWLEDGMENTS xv DEDICATION xvi CHAPTER 1 INTRODUCTION Campos Basin Geology Campos Basin Reservoirs Geology of Campos Basin s Turbidities Field Characteristics Depositional Model Petroleum System Applications of Ocean-Bottom-Cable OBC Acquisition Processing Research Objectives CHAPTER 2 INTERPRETATION OF PRE-STACK DATA P-S Pre-Stack Data Conversion from P-S to P-S at P-P Time CHAPTER 3 INVERSION iv

6 3.1 Pre-stack Inversion Pre-Stack Inversion Theory Inversion Problem Pre-Stack Inversion Successful Case Histories Model-Based Simultaneous Inversion Pre-Stack Inversion Steps Angle Domain Wavelet Extraction Correlating Wells and Horizons Building the Initial Model Analyzing Inversion s Parameters CHAPTER 4 INVERSION RESULTS Quality Control of Pre-stack Inversion P-P Pre-Stack results P-S Pre-Stack results P-P Minus P-S Volume from Pre-Stack Inversion CHAPTER 5 PREDICTING POROSITY FROM PRE-STACK ATTRIBUTES Porosity Logs Porosity Prediction CHAPTER 6 RESERVOIR CHARACTERIZATION CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS Conclusions Recommendations v

7 REFERENCES CITED vi

8 LIST OF FIGURES Figure 1.1 Campos Basin location (modified from Bruhn & Johann, 2008) Figure 1.2 Figure 1.3 Figure 1.4 Figure 1.5 Figure 1.6 Figure 1.7 Figure 1.8 Geologic evolution of the Brazilian continental margin (Ponte & Carozzi, 1980) Campos Basin stratigraphic chart (modified from Guardado et al., 2000). The studied field is a turbidite reservoir from the Santonian/Campanian period Generalized geological section for the eastern Brazilian marginal basins (Bruhn et al., 1998). The studied reservoir is from the Marine Transgressive Megasequence Distribution of the Brazilian total reserves, according to the major reservoir types. Turbidites are, by far, the most important reservoirs (modified from Bruhn & Johann, 2008) Architectural types of Campos turbidites reservoirs. (1) unconfined gravel/sand-rich, (2) unconfined sand-rich lobes, (3) trough-confined sand-rich lobes, and (4) sand-rich channel fills and splays. The studied reservoir is classified as a trough-confined sand-rich lobes (Santos et al., 2000) Architectural types of the field reservoir. (1) Canyon-filling deposits (massive thick sandstones and deformed shales); (2) Slip deposits (sandstones and deformed shales); (3) Channel system deposits (medium sandstones and deformed shales); (4) Divergent channel deposits (fine to medium massive sandstones with plane-parallel stratification at the top) (Voelcker et al., 2000) Tectono-stratigraphic chart of the Campos Basin, showing the stratigraphic location of the different elements making up the three petroleum systems (potentially) present in the basin (modified from Belinger & Cloetingh, 2012) Figure 1.9 Miniature OBS acquisition pre-plot.(acquisition-report, 2005) Figure 1.10 PP pre-stack processing flow (Processing-Report, 2010) Figure 1.11 PS pre-satck processing flow (Processing-Report, 2010) vii

9 Figure 2.1 Figure 2.2 Pre-Stack P-P seismic sections (NW-SE) showing: 1- Near Pre-Stack data; 2- Mid Pre-Stack data; 3 Far Pre-Stack data Seismic section from NW to SE in the study area showing: 1- Pre-Stack data: Blue-marker (blue), Pebbly (brown), Reservoir (orange), Carbonate Platform (cyan), top of salt (purple) and base of salt (pink).. 23 Figure 2.3 Structural map (two-way time) showing the top of the reservoir Figure 2.4 Figure 2.5 Figure 2.6 Figure 2.7 Figure 2.8 Figure 2.9 Time structural map (two-way time) showing the top of carbonate platform Maximum trough amplitude map (P-P data) from 10 ms below the reservoir: 1- Near Pre-Stack data; 2- Mid Pre-Stack data; 3- Far Pre-Stack data Pre-Stack P-S (converted to P time) seismic section showing: 1-Near Pre-Stack data; 2-Mid Pre-Stack data, 3 Far Pre-Stack data Maximum trough amplitude map (P-S data) from 10 ms below the reservoir: 1- Near Pre-Stack data; 2- Mid Pre-Stack data; 3- Far Pre-Stack data Seismic section from NW to SE in the study area showing: 1- P-S Pre-Stack data and 2- interpreted data: Blue-marker (blue), Pebbly (brown), Reservoir (orange), Carbonate Platform (cyan), top of salt (purple) and base of salt (pink) Well-11 P-wave, S-wave and density logs correlating to P-P and P-S (P-P time) angle gather data Figure 3.1 Model-based simultaneous pre-stack inversion main workflow Figure 3.2 Figure 3.3 Angle gather section at inline 1358 and well-11 showing the sonic log. The angle gather shows that I have useful data out to about 33 degrees. This should be good for extracting P-impedance and S-impedance volumes. The reservoir is the strong through event near 2600 ms P-S angle gather section at inline 1358 and well-11 showing the S-wave log (green), density log (blue) and P-wave log (red). P-S data were converted to P-P time. P-S data is noisier than the P-P section. The reservoir is the yellow horizon near 2600 ms viii

10 Figure 3.4 Figure 3.5 Figure 3.6 Figure 3.7 Figure 3.8 Figure 3.9 Figure 3.10 P-P wavelets extracted from the P-P angle gathers seismic data in a time window between 2400 ms and 3000 ms. Above, the wavelet in time and in below the wavelet amplitude spectrum P-S wavelet extracted from the P-S angle gathers seismic data in a time window between 2400 ms and 3000 ms. Above, the wavelet in time and below the wavelet amplitude spectrum P-P synthetic-field data match at well-11. In blue is the synthetic seismic generated from the P-wave and density logs. In red the trace from the seismic extracted around the well and repeated five times. In black are the P-P angles gathers traces at the well location. Correlation coefficient is 88.6 percent, main reservoir in orange, S-wave log in green, P-wave log in red and density logo in blue Maximum trough amplitude map (P-P data) from 10 ms below the reservoir in the Mid Pre-Stack data, showing the wells 11, 01-D and 02-D that were used as input to the inversion process Inline 1358, showing the initial P-impedance model, the horizons used to construct it and the well 11, one of the three wells used to build the model Inline 1330, showing the initial S-impedance model, the horizons used to construct it and the well 01-D, one of the three wells used to build the model Crossplot analyses from P-P angle stack inversion. Shown in the red circle are those points that correlate to the hydrocarbon content in the reservoir. On the left, ln(z S ) vs ln(z P ). On the right, ln(ρ) vs ln(z P ).. 44 Figure 4.1 Inversion analyses at well 11, showing the excellent correlation (.95) between the synthetic (red) and the real angle stack data (black). On the left, P-impedance, S-impedance and density inverted logs (red) overlaying the original logs (blue) and the initial model log (orange) Figure 4.2 Inversion analyses at well 01-D (on the top) and at well 02-D (below), showing the excellent correlation.98 (well-01-d) and.98 (well-02-d) between the synthetic (red) and the real angle stack data (black). On the left, P-impedance, S-impedance and density inverted logs (red) overlaying the original logs (blue) and the initial model log (orange) ix

11 Figure 4.3 Minimum negative amplitude map extracted in a 10 ms below the top of the reservoir from the P-P angle gather seismic data. The dash line contours represent the extent of both main reservoirs in the field as determined from well control Figure 4.4 Minimum inverted elastic impedance attribute map, extracted in a 30 ms window below the top of the reservoir.the dash line contours represents both main reservoirs in the field Figure 4.5 Figure 4.6 Figure 4.7 Figure 4.8 Inline 1358 showing the inverted elastic P-impedance data. The top of reservoir is the black dashed line interpreted in the seismic data. The bottom of the reservoir is the red dashed line interpreted in the inverted P-impedance volume. From right to left, well-11, that was used in the inversion process, and well-34-d, a blind well, showed good correlation with the inverted volume Minimum inverted V p/v s ratio attribute map, extracted in a 30 ms window below the top of the reservoir. The dash line contours represents both main reservoir in the field P-S synthetic-trace correlation data match at well-11. In blue is the synthetic seismic generated from the P-S-wave at P-P time. In red the trace from the seismic extracted around the well and repeated five times. In black are the P-S angles gathers traces, converted to P-P time, at the well location. Correlation coefficient is.83, the main reservoir is highlighted in orange, S-wave log in green, P-wave log in red and density log in blue Well-11 P-wave, S-wave and density logs correlating to P-S at P-P time three angle gather data, showing an excellent correlation of Figure 4.9 Minimum inverted shear-impedance attribute map, extracted in a 30 ms window below the top of the reservoir. The dash line contours represents both main reservoirs in the field Figure 4.10 Figure 5.1 Minimum EI SI attribute map attribute map, extracted in a 30 ms window below the top of the reservoir. The dash line contours represents both main reservoirs in the field From left to right, wells 01-D, 02-D, 11 and 17, showing the density logs that were used to compute the porosity logs. The main reservoir is highlighted in all wells in the orange box x

12 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Figure 5.6 Figure 5.7 Figure 5.8 Figure 5.9 Figure 5.10 Neutron-density crossplot at wells 01-D, 02-D and 11. The colors of data points are given by gamma-ray logs. Overlaying the plot is a pure sandstone lithology line, where the porosity is indicated along the line. The red reservoir points has an excellent correlations not only with the sandstone line, but also with the porosity lines From left to right, wells 01-D, 02-D, 11 and 17, showing the computed porosity logs (red) and the inverted P-impedance (blue) extracted at those well locations. The main reservoir is highlighted in all wells in the orange box Data-driven statistical interpretation - modify from Soubotcheva & Stewart (2004) The results of step-wise linear regression applied to porosity log estimation problem. The first 7 attributes showed the highest correlation with the porosity logs Prediction error plot, where the horizontal axis shows the number of attributes used in the prediction. The vertical axis is the root-mean-square prediction error for that number of attributes. Original wells are in black and validation dataset (predicted data) are in red From left to right, porosity logs and the most valuable attributes: amplitude weight cosine phase, cosine instantaneous phase, filter 5/10 15/20, filter 15/20 25/30 and inverted P-impedance at well-11. Each sample of porosity log is related to a group of neighboring samples on the seismic attributes Crossplot of actual and predicted porosity using multiattribute transforms. Data points from analysis zone of each well are shown in one color Application of multiattribute transforms using 5 attributes to predict the porosity. Low-frequency trends are adequately predicted, but the transform fails to predict the extreme values. Correlation is valid only in the analysis window Crossplot of actual and predicted porosity using probabilistic neural networks for 5 attributes. Data points from analysis zone of each well are shown in one color. From PNN s prediction, data points are very close to the regression line and scatters are minimum xi

13 Figure 5.11 Figure 5.12 Figure 5.13 Figure 5.14 Figure 6.1 Figure 6.2 Figure 6.3 Figure 6.4 Application of PNN using 5 attributes to predict porosity. Extreme values are predicted with higher accuracy. Correlation is valid only in the analysis window Validation results for PNN at well-13, using 5 attributes to predict porosity. The cross-correlation coefficient is 0.60 between the original logs (black) and the predicted porosity log (red). The main reservoir is highlighted between orange lines NW-SE arbitrary line from the predicted porosity volume generated based on linear multiattribute step-wise regression and PNN method using 5 attributes. The top of reservoir is the dotted black line and the blue logs are the original porosity logs at wells 15, 11, and 18 from left to right. Blind wells 15 and 18 shown an excellent correlation with the predicted porosity volume Porosity map, extracted 30 ms below the reservoir, generated based on linear multiattribute stepwise regression and PNN method using 5 attributes. The map shows an excellent correlation with both main reservoir areas. The dash line contours represents both main reservoir in the field EI minimum attribute map extracted over a window 30 ms below the top of reservoir. The upper zone reservoir is highlighted in black-dash lines and represents the main reservoir. The divergent channel reservoir is highlighted by black-rounded-dots, representing the E-W divergent channel system EI minimum attribute map extracted in a 40 ms window below the reservoir plus a 20ms constant time interval. The reservoir s lower zone is highlighted by black-rounded-dots Inline 1391, showing the EI volume. The reservoir s upper zone is highlighted by black-dashed lines and the reservoir s lower zone is highlighted by the black-rounded-dots. The red-dark lines are indicating the main faults present in the field SW-NE arbitrary line crossing the main reservoir, showing the EI volume, gamma-ray logs (blue) and P-wave logs (red). The reservoir s upper zone is highlighted by black-dashed lines and the reservoir s lower zone is highlighted by the black-rounded-dots. Both canyon-filling deposits and mass transport deposits from the lower zone are interpreted in the EI volume xii

14 Figure 6.5 Figure 6.6 Figure 6.7 Figure 6.8 Figure 6.9 Figure 6.10 Figure 6.11 Figure 6.12 SI minimum attribute map extracted in a 60 ms window below the reservoir. Note that the main faults studied in the research area have an excellent correlation with SI attribute (black dashed lines). The reservoir s lower zone has its thicker and lesser porous compartment between the highlighted blue-dashed lines EI SI minimum attribute map extracted within 60 ms window below the reservoir. The both main reservoir s limits are highlighted in black-dashed lines Porosity maximum attribute map extracted in a window 30 ms below the top reservoir. The main reservoir is highlighted in red-dash lines. The E-W reservoir is highlighted in red-round-dot lines. Note the southern anomaly highlighted by the black-dash line as a possible field extension Inline 1393, showing the predicted porosity volume. Porosity values ranging from 0.12 (purple) to 0.28 (dark-red). The reservoir s upper zone has its top at the yellow line and its bottom interpreted at the red-dashed lines. The lower zone is highlighted in the black-dashed lines.. 83 Predicted porosity versus inverted EI SI crossplot from the main reservoir, where data points represent the elastic impedance shown in the color bar. Good quality reservoir facies are highlighted in yellow, and poor quality reservoir facies are highlighted in orange Inline section 1391 extracted 100 ms below the top of reservoir, showing the high porosity facies (yellow) and the poor porosity facies (orange). In the upper right corner is shown the porosity map extracted 30 ms below the reservoir and the location of the inline section (black line) Predicted porosity versus inverted EI SI crossplot from the E-W divergent channel reservoir, showing good quality reservoir facies (highlighted in the red circle) and poor reservoir quality facies (highlighted in the yellow square) EI SI versus EI where data points represent the predicted porosity shown in the color bar, values ranging from 0.12 (purple) to 0.28 (dark-red). The reservoir is highlighted in the red ellipse, combining the smallest values from EI and EI SI and the highest porosity values xiii

15 LIST OF SYMBOLS P-wave Velocity V P S-wave Velocity V S Density ρ Ratio between S-wave and P-wave velocity γ P-Impedance S-Impedance Z P Z S Natural logarithm of P-Impedance Natural logarithm of S-Impedance L P L S Natural logarithm of Density L ρ P-Reflectivity R P S-Reflectivity R S Density-Reflectivity R D Angle Dependent P-wave Reflectivity Angle Dependent Converted S-wave Reflectivity R P P (θ) R P S (θ) Angular coefficient of the linear curve from the cross-plot between Z S and Z P K Angular coefficient of the linear curve from the cross-plot between ρ and Z P m Constant angular coefficient from the cross-plot between Z S and Z P K c Constant angular coefficient from the cross-plot between ρ and Z P m c xiv

16 ACKNOWLEDGMENTS I would like to thank Petrobras for being sponsoring at CSM, my advisor Dr. Thomas Davis, all the Geophysics professors in the Green Center and all the international students that now are my closest friends. xv

17 To my family and specially to my beloved women: Leticia and Lavinia. xvi

18 CHAPTER 1 INTRODUCTION The Campos Basin is located in southeastern Brazil, mostly offshore of the states of Rio de Janeiro and Espirito Santo occupying an area of km 2 (Figure 1.1) between latitudes 20 o and 22 o south and longitudes 40 o and 42 o west (Bruhn & Johann, 2008). Figure 1.1: Campos Basin location (modified from Bruhn & Johann, 2008). The Campos Basin is responsible for approximately 70% of Brazil s 2.0 million bpd daily oil production. The Campos Basin fifty-eight oil fields are found between 50 and 140 km off the Brazilian coast, occurring in water depths up to 2400 m (Bruhn & Johann, 2008). 1

19 These fields produce from a variety of reservoirs, but are mostly late Albian to early Miocene siliciclastic turbidites. North and south boundaries of the basin are: the Vitoria High that separates the Campos Basin from the Espirito Santos Basin in the northern boundary and the Cabo Frio High in the south that separates Campos Basin from the Santos Basin. The Campos Basin is open to the east. The basin has a small onshore portion (500 Km 2 ) where the first exploratory well was drilled in 1959 (Bruhn et al., 2003). 1.1 Campos Basin Geology The tectonic and sedimentary evolution of the Campos Basin is related to the Neocomian age break-up of the Gondwana Supercontinent, and the subsequent opening of South Atlantic Ocean. The Neocomian break-up involved the splitting of the African and South American plates as a result of doming, rifting, and drifting of the continental crust. It is a typical continental margin basin of the Atlantic type (Ponte & Carozzi, 1980). According to Ponte & Carozzi (1980) the Campos Basin has four main stages of evolution: (1) pre-rift arch stage, (2) an intracratonic rift-valley stage, (3) proto-oceanic gulf stage and (4) continental margin stage, as shown in (Figure 1.2). The first event, the pre-rift arch stage, occurred from the Late Permian to the Late Jurassic. The sediments from this stage were not deposited in the Campos Basin because it was located in an interdome position. The intracratonic rift-valley stage in the Early Cretaceous time was created by increasing in the tensional forces in the arching stage. This tensional tectonism led to extrusions, volcanic activities, rifting (graben and horst structures) and transform faults related to tectonism. In the proto-oceanic gulf stage, occurring during Aptian time, the first marine transgression invades the rift systems. This stage was an ideal site for the deposition of thick evaporite sequences where tectonism was rare and marine circulation was restricted. Indeed, this event represents the transition from riftvalley to continental margin basin stage. This last stage comprises two phases of tectonic evolution: the narrow and open ocean phase. From Albian to Santonian time, open marine conditions were dominant as the proto-oceanic gulf developed into a shallow, free circulating 2

20 Figure 1.2: Geologic evolution of the Brazilian continental margin (Ponte & Carozzi, 1980). and clear marine water. This environment offered ideal conditions for the deposition of platform carbonates. As the tectonic activity remained weak in this stage, the basin had a relatively low influx of siliciclastics. In Cenomanian time, the progressive widening of the ocean promoted seaward tilting eastward due to the subsidence of the ocean floor. The previous marine and carbonates environment were succeeded by deposition of continental margin sediments (Ponte & Carozzi, 1980). 3

21 Campos Basin is among those typical examples of passive margin basins. They are the outcome of a succession of thermomechanical processes including rifting, crustal extension and rupture. Neocomian to Barremian rifting created extensive accommodation space for Mesozoic-Cenozoic successions generating over than 10 km of sediments, as shown in the stratigraphic chart of (Figure 1.3) from (Guardado et al., 2000). Thermal-isostatic subsidence, enhance due to sediment loading subsidence, followed the basin filling sedimentation (Bruhn & Walker, 1995). Figure 1.3: Campos Basin stratigraphic chart (modified from Guardado et al., 2000). The studied field is a turbidite reservoir from the Santonian/Campanian period. 4

22 The general Late Jurassic to Recent stratigraphy of the eastern Brazilian marginal basins can be subdivided into six megasequences (Figure 1.4) (Bruhn et al., 1998):(1) Continental Pre-Rift Megasequence (Late Jurassic to Early Neocomian), (2) Continental Rift Megasequence (Early Neocomian to Early Aptian), (3) Transitional Evaporitic Megasequence (Middle to Late Aptian), (4) Shallow Carbonate Platform Megasequence (Early to Middle Albian), (5) Marine Transgressive Megasequence (Late Albian to Early Tertiary), and (6) Marine Regressive Megasequence (Early Tertiary to present). Figure 1.4: Generalized geological section for the eastern Brazilian marginal basins (Bruhn et al., 1998). The studied reservoir is from the Marine Transgressive Megasequence. 1.2 Campos Basin Reservoirs Offshore exploration in the Campos Basin started in 1968 when the first 2D seismic data were acquired. The first well was drilled in 1971 and the first discovery dates from 1974 at a water depth of 120 m (Bruhn et al., 2003). Ever since, the Campos Basin is the most prolific and productive oil basin in Brazil, producing from a variety reservoirs including fractured basalts and coquinas from the Continental Rift Megasequence, Albian calcarenites from 5

23 the Shallow Carbonate Platform Megasequence and siliciclastic turbidites from the Marine Regressive and Transgressive Megasequence. The most important petroleum reservoir in Campos Basin is the turbidites, as showed in the pie chart (Figure 1.5) from Bruhn & Johann (2008). Figure 1.5: Distribution of the Brazilian total reserves, according to the major reservoir types. Turbidites are, by far, the most important reservoirs (modified from Bruhn & Johann, 2008). 1.3 Geology of Campos Basin s Turbidities By definition, the term turbidite should refer strictly to those deposits that formed from turbulent suspension by turbidity currents (Sanders, 1992). And the generally accepted definition for turbidity currents are a type of sedimentgravity flow with Newtonian rheology and turbulent state in which the principal sediment-support mechanism is the upward component of fluid turbulence (Shanmugam, 2001). Two famous and worldwide turbidite geological models, proposed by Vail et al. (1991) and Mutti (1992) explain the majority of the turbidite deposits used in petroleum exploration. According to Vail s model, eustasy and relative sea level falls are the major geological events that trigger turbidity currents and the associated depositional systems. On the other hand, 6

24 Mutti s model explains those associated with large shelf-break slope, slumps and catastrophic floods. Mutti s model was mainly based on outcrop of ancient basin fills in Apennines and Pyrenees and its subdivides a fan system into canyon, inner-, middle- and outer-fan facies associations passing distally into basin-plain strata. Studies in Campos Basin s turbidites have shown that its reservoirs comprise different types and also can be complex and heterogeneous. Grain size, net-to-gross ratio, external geometry, depositional processes, and depositional setting are among properties that s been used to discriminated the types of turbidites. The main types of turbidite reservoirs from the Campos Basin include (Santos et al., 2000): (1) unconfined gravel/sand-rich, heavily dissected by younger mud-filled channels, (2) unconfined sand-rich lobes, (3) trough-confined sand-rich lobes, and (4) sand-rich channel fills and splays. The geologic pattern and the characteristics of these turbidite types are showed in the Figure 1.6. Those turbidites reservoir are mainly composed by individual lobes that can be 50 m thick, 2-8 km wide, and 5-12 km long sandstone bodies (Santos et al., 2000). 1.4 Field Characteristics The turbidite sandstones that contain the hydrocarbons in this deep-water field from Campos basin were deposited in different geological periods from Cretaceous to Tertiary. The main reservoirs in oil volume are turbidites from Santonian to Late-Campanian period. Secondarily, there is oil in the Middle Eocene reservoir and non-associated gas reservoir in the oligocene turbidites. There are still occurrences of little significant volumes in the reservoirs belonging to turbidites from Turonian-Maastrichian ages. This thesis mainly focuses on the Late Santonian to Campanian period reservoir. This reservoir was discovered in The field production started in 1985 and water injection started in The water depth ranges from 320 m to 780 m. The field has excellent petrophysical properties with an average porosity of.27 and average permeability of 2500 md. The oil is 29 o API and 2.1 cp at reservoir conditions. The reservoir has good lateral communication, even though it is cut by several normal faults (Lima & Malagutti, 2010). 7

25 Figure 1.6: Architectural types of Campos turbidites reservoirs. (1) unconfined gravel/sandrich, (2) unconfined sand-rich lobes, (3) trough-confined sand-rich lobes, and (4) sand-rich channel fills and splays. The studied reservoir is classified as a trough-confined sand-rich lobes (Santos et al., 2000). 1.5 Depositional Model The reservoir is Santonian / Campanian age and is related to a regional canyon where the turbidites were deposited. The external geometry of the accumulation has a canyon shape in which amalgamated turbidite channel deposits formed, which resulted in high sandstone shale content with excellent permeability-porosity. The maximum thickness of sandstone reaches 192 m in the center of the channel - canyon. The reservoir is mainly subdivided into four architectural elements (Voelcker et al., 2000), whose vertical stacking of bottom-up is as follows (Figure 1.7): (1) Canyon-filling deposits (massive thick sandstones and deformed shales); (2) Slip deposits (sandstones and deformed shales); (3) Channel system deposits (medium sandstones and deformed shales); (4) Divergent channel deposits (fine to medium massive sandstones with plane-parallel stratification at the top). The upper zone has great 8

26 lateral distribution, and the lower zone is thicker, more restricted in areal extent and also associated with an expressive channel. The beginning of reservoir sedimentation was formed by canyon filling sediments from turbidity currents and slides in highly confined environment. It was filled by medium to coarse sandstones, poorly selected, with a high percentage of clay, with deformations associated with muddy slumps. Slip deposits extend throughout the entire field, with a lobe shape and a significant thickness geometry (25 m) in the proximal region (above the canyon), pinching through southeast direction. The slipping or slump deposits created thin to medium plane surface throughout all the area (seismic unconformity) where later some channels were active, eroding and depositing thin turbidites. Figure 1.7: Architectural types of the field reservoir. (1) Canyon-filling deposits (massive thick sandstones and deformed shales); (2) Slip deposits (sandstones and deformed shales); (3) Channel system deposits (medium sandstones and deformed shales); (4) Divergent channel deposits (fine to medium massive sandstones with plane-parallel stratification at the top) (Voelcker et al., 2000). 1.6 Petroleum System During the Continental Rift Megasequence, lacustrine calcareous Barremian deposits such as black shales from the Lagoa Feia Formation were deposited overlying Neocomian 9

27 basalts (Cainelli & Mohriak, 1999). This is the major source rock proven in the Campos Basin (Figure 1.8). Beglinger & Cloetingh (2012) analyzed the tectonic subsidence history of the Campos Basin and developed a tectonic model that explains the observed subsidence history. They worked in subsidence and maturity reverse and forward models in order to analysis not only thermal implications but also the source-rock maturation patterns in Campos basin sourcerocks. Their results showed that the lacustrine shales of the syn-rift Lagoa Feia Fm. are the major source rocks proven in the Campos Basin. The marine shales/marls of the early post-rift Macae Fm. are locally mature to generate oil, and significant section of the post-rift Carapebus/Ubatuba Fm. may also generate oil. Guardado et al. (2000) showed that the Lagoa Feia Formation source rock has TOC of 2 to 6 wt percent and hydrogen indices (HI) higher than 900 mg HC/mg TOC. Those parameters are of a Type I kerogen. The migration pathway from the source rock Lagoa Feia Formation to Santonian/Campanian turbidites must occur between the syn-rift and post rift through the evaporite layers. Extensional halokinetic movements created windows in the evaporite layers, allowing upward migration along salt-induced faults (Beglinger & Cloetingh, 2012). The trap system is composed of both structural and stratigraphic components coincident with the evolution of the deep sea fan complex. Finally, deep-water marine shales are the petroleum system seal rocks. 1.7 Applications of Ocean-Bottom-Cable Since the end of twentieth century, the oil and gas industry as showing an increasingly interest in seismic imaging within the marine environment using four-component (4-C) oceanbottom-cable (OBC) recording. Equipped with a single hydrophone (pressure detector) plus a three-component (3-C) geophone (particle velocity detector) the P-wave image obtained from OBC data is superior to the image obtained from streamer seismic data, as showed by Stewart et al. (2007). While the hydrophone measures wave field pressure, the 3-C geophone measures the velocity of a wave s displacement and also detects the signal s difference between 10

28 Figure 1.8: Tectono-stratigraphic chart of the Campos Basin, showing the stratigraphic location of the different elements making up the three petroleum systems (potentially) present in the basin (modified from Belinger & Cloetingh, 2012). 11

29 upgoing and downgoing compressional waves (Hoffe et al., 2000). Among several advantages from using 4-C OBC seismic recording instead streamer conventional acquisition, these are the three main advantages: (1) dual-sensor summation (hydrophone + vertical geophone signals) for the suppression of receiver-side multiples; (2) utilizing P-S wave conversions for enhanced imaging; (3) attenuation of free-surface multiples when combined with towedstreamer recordings. Stewart et al. (2007) studying OBC seismic data from the Beryl Alpha field in the U. K. North Sea and concluded that the vertical component of the geophone was the key factor to improved the seismic image. They also concluded that OBC s fold and wide azimuth data improved the image slightly. 1.8 OBC Acquisition The OBC data that I worked in this research was conducted by PGS Geophysical at the direction of Petrobras and was acquired from April to June The 3-D, 4-C oceanbottom-cable survey (OBC) was conducted by four vessels: the Ocean Explorer as recording vessel, the Falcon Explorer as shooting vessel, the Bergen Surveyor as cable vessel and Marimar XIII as chase vessel. The survey consisted of 33 swaths split in three tiers with receiver lines reaching 6 km length (Figure 1.9). PGS used 5 cables measuring 6000 meters 250 meters apart from each other. Receiver station spacing was 25 meters, summing 240 receivers stations per receiver line. Inline offset length was 6400 meters, while cross line offset was 775 meters. The source was positioned 5 meters deep and the shot interval was 87.5 meters. Data was recording at 2 ms sampling rate within a record length of 10 seconds (Acquisition-Report, 2005). The field had a total of four mobile drilling rigs and one permanent production rig. On the northeast edge of the survey there was a series of pipelines from one producing well crossing the acquisition area. Meanwhile, seismic interference and noise from nearby vessels were always present during the acquisition. The main objective of the QC control was to monitor and evaluate these noise levels in real-time. There were also strong water bottom 12

30 Figure 1.9: Miniature OBS acquisition pre-plot.(acquisition-report, 2005) multiples occurring on the P-wave data. However, the summation of the hydrophone and vertical geophone data helped to attenuate these multiples as will be shown in the processing section. Seismic interference (SI) was the most prevalent form of noise during the acquisition program. PGS used a noise prediction program which calls SINK (Seismic Interference Noise Killer) to attenuate the remained SI in the data. The results are better when SI from shot to shot arrives at different times for consecutive records (Acquisition-Report, 2005). 1.9 Processing The OBC survey acquired over the target field was processed in 2010 by WesternGeco. In this research I used the P-P, and the converted P-S pre-stack data. An overview of the seismic processing flow is shown in Figure 1.10 and Figure 1.11 from the WesternGeco Processing-Report (2010). 13

31 Figure 1.10: PP pre-stack processing flow (Processing-Report, 2010). 14

32 Figure 1.11: PS pre-satck processing flow (Processing-Report, 2010). 15

33 In the center of the entire OBC method and its processing flow is the concept of the dual sensor summation. Due to the fact that the hydrophone records pressure (a scalar) and the geophone records velocity (a vector), the upgoing, P-wave on the hydrophone and vertical geophone should be of the same polarity. On the other hand, the response of the down-going multiple reverberations should be of opposite polarity. Then, applying the dual sensor summation process helps to eliminate surface-related multiples Processing-Report (2010). Kirchhoff Pre-Stack Anisotropic Time Migration was the migration method used in this OBC data. Depending on the structural complexity of the area, post-stack migration becomes less accurate. Pre-stack imaging in this area is a necessary step to better image dipping structures, avoiding conflict dips and velocity analyses errors from post-stack methods (Processing-Report, 2010). AVO angle trace generation was another important step in the processing flow, where traces recorded at fixed offsets were transformed in to traces regarding its angles of incidence. In this process, traces within a desired range of reflection angles were stacked, producing an angle trace, and then, repeating this process for different reflection angles, angle trace gather were produced (Processing-Report, 2010). For the converted P-S angle stack data, there were three main steps exclusively performed for converted waves from 3D-4C OBC acquisition technique: crossline geophone correction, horizontal geophone rotation and the common conversion point binning method (CCP binning). Crossline geophone correction accounts for the coupling correction from the geophone with the seabed. After the coupling correction step, the geophones were rotated into the source-receiver (radial) direction and its perpendicular (transverse) direction. As the outcome from rotation and assuming isotropic conditions of the subsurface, all the energy on the radial will represent P-SV mode conversion. This is the component used to produce the conventional PS volume. Finally, another processing step inherit from OBC technique is CCP binning. The converted wave has an asymmetric ray-path nature. In order to mini- 16

34 mize the asymmetric nature of the, CCP binning computes the shift of the conversion (and reflection) point from the source-receiver midpoint location (Processing-Report, 2010) Research Objectives Working with pre-stack P-P and P-S data from 3-D, 4-C OBC seismic data my main objective in this research is to better to characterize a thin turbidite reservoir in the deepwater Campos basin, Brazil. In order to achieve this goal, I performed: Data and seismic analysis; Interpretation and well correlations; Pre-Stack P-P Inversion using P-P angle Gathers; Pre-Stack P-S Interpreting and converting to P-P time; Joint PP-PS Pre-Stack inversion; Facies and porosity analyses using using the pre-stack inverted volumes as input. 17

35 CHAPTER 2 INTERPRETATION OF PRE-STACK DATA The multi-component 3D, 4C pre-stack OBC seismic data were provided by Petrobras and the SEG-Y were loaded into the seismic project. The pre-stack data has four main angle stacks: near (01 o -15 o ), mid-near (11.5 o o ), mid (20 o -30 o ) and far (27.5 o o ). Figure 2.1 shows a seismic section, representing near, mid and far P-P seismic data angle stack sets. Ribeiro (2011) did an excellent interpretation on the main horizons while working on post-stack data. Using vertical wells with sonic and density logs to create synthetic seismic traces, he tied the wells with the seismic data and interpreted the main horizons. I imported those horizons to my data set project and started my seismic analyses. The top of the reservoir is a well defined trough amplitude, that we can distinguished in those 3 angles data sets (Figure 2.1), due to the lower impedance of the reservoir compared to the surrounding shale. The main horizons, as shown in Figure 2.2: the blue marker, represented by one strong peak that is correlated to the lower Oligocene carbonate event, a well known unconformity in the Campos Basin; the top of the pebbly zone, represented by one well-marked peak on the seismic. It was easily interpreted and correlated because all the wells drilled to the reservoir have the pebbly zone identified on the logs; the top of the carbonate platform, one important stratigraphic and structural unconformity, represented by a strong peak on both synthetics and actual seismic data. In contrast to the pebbly zone, the carbonate platform was not penetrated by most of the wells, but a few wells have logged this horizon, that has higher impedance than 18

36 the shale and the reservoir above it. Due the unconformity nature and its structural control, the carbonate was, by far, the most difficult horizon to interpret. the top of the reservoir is well represented in the P-P seismic data by a trough followed by a peak at the base of the reservoir. The reservoir has a lower impedance than the overlying shales, due the presence of the oil and high porosity in the sandstone. top and bottom of the evaporite sequence is well-marked on the 3D seismic data. Both horizons were important not only to better understand the tectonics within the carbonate platform but also to help in the P-S conversion to P-P time. After re-interpreting and analyzing these horizons, the next step was to create amplitude maps from the angle stacks present in the project. Amplitude analyses are very important to this reservoir, as the discovery and development of this field was based on the seismic maximum negative or minimum amplitude map of the top of the reservoir (Ribeiro, 2011). The structural map from the reservoir is shown in Figure 2.3 and its deposition and structure is controlled by the carbonatic platform below (Figure 2.4). With these excellent data sets, it is possible to better understand the reservoir, firstly doing amplitude analyses. These analyses were driven from amplitude maps extracted 10 ms below the reservoir (Figure 2.5). From P-P amplitude maps we can better delineate the boundaries and the channels that form the reservoir. 2.1 P-S Pre-Stack Data The P-S angle stack seismic data (Figure 2.6) show the main structures as shown in the P-P data, but the reservoir is more difficult to interpret mainly because the P-S data responds to different reflection coefficients than the P-P data. From the P-S amplitude maps we can start to do some correlations between the main structures that controlled the reservoir sedimentation and the carbonate platform (Figure 2.7). I used the mid pre-stack data to interpret the main horizons in the P-S (at P-S time) data. As initial input, I used the same horizons that Ribeiro (2011) interpreted in his research 19

37 using post-stack data. The pre-stack data has better definition in areas with dipping events and is highly affected by structural deformation. P-S data aren t as well defined as P-P data, but horizon interpretation was possible. Following the main horizons that I interpreted in the P-P data, I identified those horizons in the P-S data (Figure 2.8), and these are the main characteristics of each one: the blue marker was easy to interpreted and is represented by one strong peak representing an unconformity in the Campos Basin; the top of the pebbly zone was also easy to identify due to the fact that this event is the strongest peak above the reservoir; the top of the carbonate platform was harder to interpret. This event showed in some areas as a strong peak, that was the guide to begin the interpretation. Again, due the unconformity nature and its structural control, the carbonate was very difficult to interpret in the P-S data. I often had to look back to my P-P data to identify this event, mainly in areas where the peak wasn t strong enough to be identified; the top of the reservoir was harder to interpret than the carbonate platform. Interpreting the P-S data, the top of the reservoir has a weak seismic signal, because of the weak shear impedance contrast between the reservoir and the overlying shale. It was, by far, the most difficult horizon to interpret in the P-S data, as I just had one well that had S-wave logs, also showing a weak trough at the top of the reservoir. the top of the evaporites sequence, wasn t easy to interpret but I was able to interpret the horizon on the 3D seismic data. These horizons will be helpful in order to convert the P-S data into P-S at P-P time, thus I m able to perform pre-stack inversion and compare the results with the P-P Inversion. I also interpreted the early and late tertiary horizons on both P-P and P-S data. Both horizons are represented by strong peaks, that are correlated with younger unconformity events in 20

38 the Campos Basin. These shallow horizons will be useful to improve the Vp/Vs ratio in the converted model used to convert P-S to P-P time. The pre-stack P-S data does show almost the main structures showed in the P-P data, but the reservoir is more difficult to interpret because the P-S data responds to a different reflection coefficient than the P-P data. 2.2 Conversion from P-S to P-S at P-P Time P-S converted data can be interpreted on their own, however better interpretations are achieved when these data are analyzed in conjunction with P-wave sections or volumes. Indeed, as a first step to understand P-S data, we need to correlate the P-P and P-S events. There are a number of ways to accomplish this interpretation technique (Stewart et al., 2002). In order to convert P-S data at P-S time into P-S at P-P time, I used these key inputs in the process: 5-five wells that contain P-wave, S-wave, and density logs, 5-five main horizons that were mapped both in P-P and P-S data and the pre-stack P-P and P-S angle gather data. After log-to-seismic calibration within both seismic data volumes (Figure 2.9), I created the first Vp/Vs model based only on the wells. Moreover, I used the mapped horizons to perform the matching horizons between P-P and P-S data, and then I updated the previous Vp/Vs model. Finally, using the updated model, I was able to convert the P-S data into P-S data at P-P time. 21

39 Figure 2.1: Pre-Stack P-P seismic sections (NW-SE) showing: 1- Near Pre-Stack data; 2- Mid Pre-Stack data; 3 Far Pre-Stack data. 22

40 Figure 2.2: Seismic section from NW to SE in the study area showing: 1- Pre-Stack data: Blue-marker (blue), Pebbly (brown), Reservoir (orange), Carbonate Platform (cyan), top of salt (purple) and base of salt (pink). 23

41 Figure 2.3: Structural map (two-way time) showing the top of the reservoir. Figure 2.4: Time structural map (two-way time) showing the top of carbonate platform. 24

42 Figure 2.5: Maximum trough amplitude map (P-P data) from 10 ms below the reservoir: 1- Near Pre-Stack data; 2- Mid Pre-Stack data; 3- Far Pre-Stack data. 25

43 Figure 2.6: Pre-Stack P-S (converted to P time) seismic section showing: 1-Near Pre-Stack data; 2-Mid Pre-Stack data, 3 Far Pre-Stack data. 26

44 Figure 2.7: Maximum trough amplitude map (P-S data) from 10 ms below the reservoir: 1- Near Pre-Stack data; 2- Mid Pre-Stack data; 3- Far Pre-Stack data 27

45 Figure 2.8: Seismic section from NW to SE in the study area showing: 1- P-S Pre-Stack data and 2- interpreted data: Blue-marker (blue), Pebbly (brown), Reservoir (orange), Carbonate Platform (cyan), top of salt (purple) and base of salt (pink). Figure 2.9: Well-11 P-wave, S-wave and density logs correlating to P-P and P-S (P-P time) angle gather data. 28

46 CHAPTER 3 INVERSION The inversion of seismic data for computing acoustic and shear impedance and density volumes offers several advantages. It facilitates integrated interpretation, improves the vertical resolution allowing sub-seismic features to be seen, and it optimizes the correlation between seismic and petrophysical proprieties of the reservoir. Changes in impedance are related to porosity, lithology, net pay, and permeability and the basis for determination of reservoir properties. These properties are very useful in estimating the volume of oil in place and can also be used during flow simulation, when the decision about the location of wells and decisions of production strategies are made. 3.1 Pre-stack Inversion Post-stack inversion mainly transforms the seismic amplitudes into a band-limited estimate of P-wave acoustic impedance, taking advantage of the low-frequency constructed from well data. In a post-stack inversion there is no mode conversion at normal incidence, and it s purely acoustic, making P-wave impedance the principal information that can be estimated from post-stack inversion of P-wave data (Chopra & Marfurt, 2007). By working with pre-stack inversion, both the low and the high frequency components of the P-wave acoustic impedance are extracted from the seismic data. The advantage of working with pre-stack data is the fact that the low-frequency components of interval velocity are implicit in the moveout curves. In addition, non-zero incidence seismic waves can also be converted to S-wave impedance giving us the opportunity to estimate elastic parameters. Using full elastic earth mode, we can estimate not only P-wave acoustic impedance but also S-wave information or Poissons ratio from pre-stack data. Those elastic parameters, within a good petrophysical and well control, provide lithology and fluid properties from the reservoir (Chopra & Marfurt, 2007). 29

47 Elastic impedance is a generalization of acoustic impedance for variable incidence angle (Connolly, 1999). Elastic impedance gives us the opportunity to calibrate and invert nonzero-offset seismic data, whilst acoustic impedance is useful for zero-offset data. Elastic impedance is a function of P-wave velocity, S-wave velocity, density, and incidence angle. The main procedure that s must be done, when correlating elastic impedance to seismic, is to assure that the stack data is in the angle stack domain rather than offset domain. 3.2 Pre-Stack Inversion Theory Using physical model described in Hampson et al. (2005), I inverted seismic angle gathers using Aki-Richards equations. The Aki and Richards equation gives an approximate relationship between the P-wave reflection coefficient and the angle of incidence, and it was re-expressed by Fatti et al. (1994) as: R P P (θ) = c 1 R P + c 2 R S + c 3 R D, (3.1) where c 1 = 1 + tan 2 θ, (3.2) c 2 = 8γ 2 tan 2 θ, (3.3) γ = V S V P, (3.4) c 3 = 0.5 tan 2 θ + 2γ 2 sin 2 θ, (3.5) and R P = 1 [ VP + ρ 2 V P ρ R S = 1 [ VS + ρ 2 V S ρ [ ρ R D = ρ ], (3.6) ], (3.7) ], (3.8) 30

48 The algorithm that I used was provided by Hampson and Russell software and is based on three main assumptions: the linearized approximation for reflectivity holds. that the reflectivity terms given in equations (1) through (3) can be estimated from the angle dependent given by the Aki-Richards equations (Aki and Richards, 2002). there is a linear relationship between the logarithm of P-impedance and both S- impedance and density, given by: ln(z S ) = k ln(z P ) + k c + L S, (3.9) ln(ρ) = m ln(z P ) + m c + L ρ, (3.10) And this relationship is expected to hold for the background wet lithologies. Given these three assumptions, we can derive a final estimate of P-impedance, S-impedance and density by perturbing an initial P-impedance model. Using the same inverted matrix approach that they used for post-stack inversion, Hampson et al. (2005) extended the theory to pre-stack inversion case, where the main objective is to solve the following equation: T (θ 1 ) T (θ 2 ). T (θ N ) c 1 (θ 1 )W (θ 1 )D c 2 (θ 1 )W (θ 1 )D c 3 (θ 1 )W (θ 1 )D = c 1 (θ 2 )W (θ 2 )D c 2 (θ 2 )W (θ 2 )D c 3 (θ 2 )W (θ 2 )D... c 1 (θ N )W (θ N )D c 2 (θ N )W (θ N )D c 3 (θ N )W (θ N )D L P L S L D, (3.11) where c 1 = 1 2 c kc 2 + mc 3 and c 2 = 1 2 c 2 (3.12) The practical approached that they used to invert the matrix above was to initialize the solution to [ LP L S L D ] T = [ ln(zp0 ) 0 0 ] T (3.13) 31

49 where ln(z P0 ) is the initial impedance model. Having this initial guess, the next step is to iterate towards a solution using the inversion method of the conjugate gradient. 3.3 Inversion Problem Tarantola (2005) states that to solve a forward problem means to predict the error-free values of the observable parameters d that would correspond to a given model m. The forward model is represented, in vector notation, as shown in the equation below. d = G m, (3.14) where G is a linear operator (in fact, a matrix), imposing the linear constraint between model parameters and observable parameters. and In this pre-stack inversion, I have the following forward model: T (θ 1 ) T (θ 2 ) d =. T (θ M ) where G = W (θ 1 ) c 1 (θ 1 )D W (θ 1 ) c 2 (θ 1 )D W (θ 1 )c 3 (θ 1 )D W (θ 2 ) c 1 (θ 2 )D W (θ 2 ) c 2 (θ 2 )D W (θ 2 )c 3 (θ 2 )D... W (θ M ) c 1 (θ M )D W (θ M ) c 2 (θ M )D W (θ M )c 3 (θ M )D m = L P L S L ρ (3.15) (3.16) (3.17) Hampson & Russell software uses the Conjugate Gradient method to solve this forward problem. This method is well described by Shewchuk (1994) and is governed by the following equation: d 0 = G T d obs (G T G) m 0 (3.18) 32

50 where G T means the transpose of the matrix G and m 0 is the initial model that s constantly updated. 3.4 Pre-Stack Inversion Successful Case Histories Ribeiro (2011) performed inversion of Campos Basin multicomponent seismic data and proved that it can significantly improve turbidite reservoir characterization. The acoustic impedance improved the reservoir image especially in the reservoir upper zone and the density volume enhanced the reservoir characterization especially in the reservoir s lower zone. Leiceaga et al. (2010) analyzed the potential of multicomponent data for density estimation by simultaneously inverting pre-stack compressional and mode converted seismic data. Using one 3C-3D seismic data in the Campos Basin, Brazil, they found that inverting solely P-P data gives them a lower correlation between the estimated density and the measured log in comparison to jointly inverting both P-P and P-S data. Their results show that adding P-S information in the inversion process improves the density and inversion resolution. Jenkinson et al. (2010) described aspects of a workflow for joint PP-PS angle-stack analysis and its application to OBC angle-stack volumes. The objective was to produce joint PPPS inversion volumes that allowed better reservoir discrimination. Their study showed that joint inversion results in that area suffer because the available P angles were too small and the S-impedance volume from a single P-S angle-stack provides the best sand-shale discrimination in the study area. Varga et al. (2007) studied and interpreted a 2D multicomponent seismic data and well logs from the Willesden Green, Alberta area to investigate an oil reservoir interval. P-P and P-S inversion was applied to the vertical and radial components to yield P and S impedance and as result they found that the calculated Vp/Vs values and the ratio of P-P impedance inversion over P-S impedance inversion were helpful for finding anomalies. Dang et al. (2010) working in one high-resolution multicomponent seismic survey performed a joint pre-stack P-P and P-S inversion and proved its effectiveness in distinguishing true and false bright spots in oil bearing sand reservoirs. Higher fluid factors, lower Vp/Vs 33

51 and Poissons ratios values in the inverted results clearly characterized the reservoir, but these characteristics were absent in the false bright spot. 3.5 Model-Based Simultaneous Inversion Model-based simultaneous inversion is the method used to integrate impedances measured in well logs and surface seismic amplitude data. In order to perform an effective inversion, good well ties and good seismic wavelets are necessary. These wavelets were computed statistically from the seismic amplitude data alone. There are major steps that improve the model-based simultaneous inversion: to make a careful interpretation of the log wells, in order to tie the available wells to the seismic data; to pick and interpret major faults and multiple horizons that will constrain the geological model; to pick also the top and bottom of anomalously high or low impedances in addition to those of exploration interest. In this research I used the Model-Based Simultaneous Inversion Method (Figure 3.1) for converting pre-stack seismic traces into P-impedance, S-impedance and density traces. In this method, I created a geological model first, using well data and interpreted horizons, and then compare the synthetic seismic data derived from this model with the real seismic data. Moreover, I use the results of this comparison to iteratively update the model in such a way as to better match the actual seismic data. 3.6 Pre-Stack Inversion Steps In my research, I used the typical work flow for pre-stack inversion, which consists of the following steps Convert seismic data into angle domain; 34

52 Figure 3.1: Model-based simultaneous pre-stack inversion main workflow Extract one or more wavelets from angle stack seismic data; Correlate each well with the seismic volume and horizons to optimize the depth-to-time conversion; Build an initial geologic model for inversion; Run inversion analysis to determine inversion parameters; Run inversion over the volume and analyze the results. As I converted my P-S angle stack data into P-P time, for P-S pre-stack inversion I used basically the same work flow described above, where I extracted statistical wavelets for P-S data in P-P domain and also created another initial model using the S wave reflectivity, P-P horizons and one well. 35

53 3.6.1 Angle Domain Pre-stack inversion actually operates in the incident angle domain, so the first step I made was to convert seismic data from offset to angle domain. In order to optimize my conversion, I used as input 4 angle gathers, where each one represents the medium value of each angle stack: for near stacks (01 o -15 o ) I used 08 o, for mid-near stacks (11.5 o o ) I used 17 o, for mid stacks (20 o -30 o ) I used 25 o and finally for far stacks (27.5 o o ) I used 33 o. In order to transform the seismic data from offset to angle domain, the software Hampson- Russel uses the Ray Parameter Method. As explained in the software s assistant, this method utilizes the main equation: sin θ = X v int t v 2 rms (3.19) where v int and v rms are respectively the interval and RMS velocities, θ is the angle at which the ray is propagating, X is the offset, and t is the two-way vertical time. As can be seen in the offset to angle equation, information on velocity is needed to correctly convert the seismic data. In this dataset, interval velocities from wells 01-D, 02-D and 11 sonic logs were used and extrapolated to entire the volume. Figure 3.2 shows a zoom in an angle domain seismic section that was converted from offset domain. Figure 3.3 shows the P-S angle domain seismic section, that was converted from P-S time to P-P time Wavelet Extraction Before building the geological model I extracted a statistical wavelet, which uses the seismic data alone to estimate the wavelet spectrum, for the inversion process. Working with a pre-stack angle gather allowed me to use different wavelets for each angle. Its main advantage is the analyses of frequency spectrum in the different angles, where the far angle wavelets tend to be lower in frequency than the near angle wavelets. For my P-P wavelet extraction I used one wavelet for each near, mid and far angle stacks and them I interpolate them (Figure 3.4). 36

54 Figure 3.2: Angle gather section at inline 1358 and well-11 showing the sonic log. The angle gather shows that I have useful data out to about 33 degrees. This should be good for extracting P-impedance and S-impedance volumes. The reservoir is the strong through event near 2600 ms. For the P-S wavelet extraction, I extracted one wavelet from the angle gather domain converted to P-P time, as shown in Figure Correlating Wells and Horizons The main horizons described herein and the well log markers interpreted from Petrobras geologists were my main guide to tie the wells and the pre-stack angle domain seismic data. Ribeiro (2011) selected three wells in his post-stack correlations: 01D has the best S-wave velocity log in the reservoir, and wells 03 and 04, two vertical wells, with excellent coefficient between the seismic and the logs. I did check those correlations in the pre-stack data, and all the synthetic seismic has a correlation coefficient higher than.80 with the P-P seismic data, using a window between 2400 ms and 3200 ms. To improve my well-tie correlations, I include in this research well 11, which has not only a good P-wave and S-wave logs, but also 37

55 Figure 3.3: P-S angle gather section at inline 1358 and well-11 showing the S-wave log (green), density log (blue) and P-wave log (red). P-S data were converted to P-P time. P-S data is noisier than the P-P section. The reservoir is the yellow horizon near 2600 ms. provided a density log correlation within the reservoir. Well 11 was the discovery well of the field and was really important to my pre-stack inversion. At well 11, the P-P synthetic seismic has a correlation coefficient of.87 with the P-P angle gathers seismic data, in a window between 2450 ms and 2700 ms (Figure 3.6). The wavelet used was the same as described in the previous section Building the Initial Model The initial geologic model is a low frequency P-impedance, S-impedance and density model, generated from well data and the main horizons interpreted in the pre-stack data. Model Based Inversion method solves for the reflectivity iteratively, searching for differences between the real angle stack traces and the synthetic traces formed from the model. In this method, the algorithm is an iterative procedure where the initial impedance from the geological model is allowed to continuously change in order to improve the match between 38

56 Figure 3.4: P-P wavelets extracted from the P-P angle gathers seismic data in a time window between 2400 ms and 3000 ms. Above, the wavelet in time and in below the wavelet amplitude spectrum. the calculated synthetic trace and the real trace. After a few attempts to reach an initial model, my best model was created using three wells 11, 01-D and 02-D as shown in Figure 3.7) and five horizons: blue marker, pebbly, top of reservoir, top of carbonate platform and top of salt. I did have to use the low-frequency component of the model to supply the low frequencies missing from the seismic, using a time domain filter to the interpolated model. My model was filtered with a 8/15 Hz high cut 39

57 Figure 3.5: P-S wavelet extracted from the P-S angle gathers seismic data in a time window between 2400 ms and 3000 ms. Above, the wavelet in time and below the wavelet amplitude spectrum. frequency. The initial impedance model is shown in Figure 3.8. To create the inital S-impedance model from my P-S angle gather data, I used the same wells as before (Figure 3.7) and four horizons: pebbly, top of reservoir, top of carbonate platform and top of salt. This model was also filtered with a 8/15 Hz high cut frequency. The initial S-impedance model is shown in Figure

58 Figure 3.6: P-P synthetic-field data match at well-11. In blue is the synthetic seismic generated from the P-wave and density logs. In red the trace from the seismic extracted around the well and repeated five times. In black are the P-P angles gathers traces at the well location. Correlation coefficient is 88.6 percent, main reservoir in orange, S-wave log in green, P-wave log in red and density logo in blue Analyzing Inversion s Parameters With my three initial models (P-impedance, S-impedance and density) built, I was one step closer to inverting the seismic data. The next and one of the most important steps in the inversion process is to run an inversion analysis at the well locations to optimize the parameters. Using the relationship shown in equations 3.9 and 3.10, I analyzed the crossplots of ln(z S ) vs ln(z P ) and ln(ρ) vs ln(z P ) (Figure 3.10). The main point of this analysis is to correctly set the parameters that control the background relationship and also are used to stabilize the inversion process. It is known that, in the absence of hydrocarbons, the relationship between these variables is linear. Deviations points from these linear trends is an indicator of hydrocarbons. 41

59 Figure 3.7: Maximum trough amplitude map (P-P data) from 10 ms below the reservoir in the Mid Pre-Stack data, showing the wells 11, 01-D and 02-D that were used as input to the inversion process. After doing the analyses and the validation of the relationships within the crossplots, I obtained the values of k c and k from 3.9 and the values of m c and m from With those relationship established, I was able to run my inversion at well locations and extend it to my entire volume. 42

60 Figure 3.8: Inline 1358, showing the initial P-impedance model, the horizons used to construct it and the well 11, one of the three wells used to build the model. 43

61 Figure 3.9: Inline 1330, showing the initial S-impedance model, the horizons used to construct it and the well 01-D, one of the three wells used to build the model. Figure 3.10: Crossplot analyses from P-P angle stack inversion. Shown in the red circle are those points that correlate to the hydrocarbon content in the reservoir. On the left, ln(z S ) vs ln(z P ). On the right, ln(ρ) vs ln(z P ). 44

62 CHAPTER 4 INVERSION RESULTS In this chapter I will discuss the results from both P-P and P-S pre-stack inversion obtained from my angle gather data. The main advantage that came from the inversion process is to interpret the reservoir as layer properties instead of interface properties. Thus the results can be correlated with the well logs and with the geologic model. Indeed, with these results from the reservoir, we can also predict petrophysical properties and that is the subject of the next chapter. 4.1 Quality Control of Pre-stack Inversion The main process that allows us to assess the quality control (QC) of the inversion process is to analyze the difference between the synthetic trace generated from the constructed model and the seismic trace used as input to the inversion process. I was also able to analyze the error between the two traces. Another useful analyses is to overlay the initial impedance model, the original impedance and the final inversion result. I did the analysis for three wells (01-D, 02-D and 11) that were used in my pre-stack inversion. Figure 4.1 shows the QC window (2450 ms to 2675 ms) used to analyze the well-11 inversion results. Figure 4.2 shows the QC window (2400 ms to 3100 ms) used to analyze well-01-d and well-02-d inversion results. In the analyses above, it is clear that the error between the synthetic data and the real angle stack data is small, indicating that the inversion has done a good job. Indeed, the acoustic impedance trace created by the inversion process is consistent with the wavelet and the input seismic trace. It s also clear that the inverted P-impedance, S-impedance and density inverted logs have an excellent correlation with the original logs. 45

63 Figure 4.1: Inversion analyses at well 11, showing the excellent correlation (.95) between the synthetic (red) and the real angle stack data (black). On the left, P-impedance, S-impedance and density inverted logs (red) overlaying the original logs (blue) and the initial model log (orange). 4.2 P-P Pre-Stack results From my P-P pre-stack angle gather, the best results that I obtained were the P- impedance and V p/v s ratio volumes. The minimum elastic impedance attribute map (Figure 4.3), extracted in a window 30 ms below the top of the reservoir interpreted in the seismic data, shows the two main reservoirs. Those two reservoirs were discovered mainly from the minimum amplitude map extracted in a 10 ms window below the top of the reservoir (Figure 4.4). The difference between the size of the attribute extraction windows is because the extraction of an interface property (seismic data) is proportional to the seismic resolution, whilst the extraction of a layer property (impedance data) is proportional to the size of the layer. 46

64 Analyzing the P-impedance section in Figure 4.5, I was able to interpret not only the top of reservoir (black dashed line interpreted in the seismic data), but also its bottom (red dashed line). Also this section shows two wells: well-11, that was used in the inversion process and well-34-d, that wasn t used in the inversion process and shows excellent correlation with the inverted P-impedance volume. Another powerful result that I had from the inversion process was the V p/v s ratio volume. This inverted result shows excellent correlation with the main reservoir, presenting values from V p/v s ranging from 1.60 to Figure 4.6 shows a minimum inverted V p/v s ratio attribute map, extracted in a 30 ms window below the top of the reservoir. 4.3 P-S Pre-Stack results From my P-S pre-stack angle gather, the first result was poor, because of the noisy near traces that had poor P to S conversion quality. To minimize this effect in my inverted results, I decided to work with only three angle gathers for my P-S data: mid-near (11.5 o o ), mid (20 o -30 o ) and far (27.5 o o ). The correlation with the well-11 (Figure 4.7) was even better than the previous one with four angle gathers. Moreover, the inversion analyses with three angle gathers was cleaner and more precise too, as shown in Figure 4.8. After performed the correlation and the inversion analyses within the P-S three angle gather data, the best result that I obtained was the shear-impedance inverted volume. The minimum shear-impedance attribute map (Figure 4.9), extracted in a window 30 ms below the top of the reservoir shows different structures than in the previous inverted P-impedance map. In this inverted shear-impedance result, the main two reservoirs were not clearly distinguished, showing that the P-S converted data is not sensitive to fluid content. However, it is possible to interpret the structural pattern that controls the regional trend in the reservoir area: the SW-NE faults structures inherit from the carbonate platform. 47

65 4.4 P-P Minus P-S Volume from Pre-Stack Inversion Apart from the lack of reservoir anomalies in the shear-impedance volume, one of the main results from this research is the attribute volume that came from the difference between the elastic and shear inverted impedance volumes (EI SI). The minimum EI SI attribute map (Figure 4.10), extracted in a window 30 ms below the top of the reservoir, shows a good correlation with both main reservoirs in the field. 48

66 Figure 4.2: Inversion analyses at well 01-D (on the top) and at well 02-D (below), showing the excellent correlation.98 (well-01-d) and.98 (well-02-d) between the synthetic (red) and the real angle stack data (black). On the left, P-impedance, S-impedance and density inverted logs (red) overlaying the original logs (blue) and the initial model log (orange). 49

67 Figure 4.3: Minimum negative amplitude map extracted in a 10 ms below the top of the reservoir from the P-P angle gather seismic data. The dash line contours represent the extent of both main reservoirs in the field as determined from well control. 50

68 Figure 4.4: Minimum inverted elastic impedance attribute map, extracted in a 30 ms window below the top of the reservoir.the dash line contours represents both main reservoirs in the field. 51

69 Figure 4.5: Inline 1358 showing the inverted elastic P-impedance data. The top of reservoir is the black dashed line interpreted in the seismic data. The bottom of the reservoir is the red dashed line interpreted in the inverted P-impedance volume. From right to left, well-11, that was used in the inversion process, and well-34-d, a blind well, showed good correlation with the inverted volume. 52

70 Figure 4.6: Minimum inverted V p/v s ratio attribute map, extracted in a 30 ms window below the top of the reservoir. The dash line contours represents both main reservoir in the field. 53

71 Figure 4.7: P-S synthetic-trace correlation data match at well-11. In blue is the synthetic seismic generated from the P-S-wave at P-P time. In red the trace from the seismic extracted around the well and repeated five times. In black are the P-S angles gathers traces, converted to P-P time, at the well location. Correlation coefficient is.83, the main reservoir is highlighted in orange, S-wave log in green, P-wave log in red and density log in blue. 54

72 Figure 4.8: Well-11 P-wave, S-wave and density logs correlating to P-S at P-P time three angle gather data, showing an excellent correlation of

73 Figure 4.9: Minimum inverted shear-impedance attribute map, extracted in a 30 ms window below the top of the reservoir. The dash line contours represents both main reservoirs in the field. 56

74 Figure 4.10: Minimum EI SI attribute map attribute map, extracted in a 30 ms window below the top of the reservoir. The dash line contours represents both main reservoirs in the field. 57

75 CHAPTER 5 PREDICTING POROSITY FROM PRE-STACK ATTRIBUTES Using the previous results from my pre-stack P-P inversion, I predicted porosity of my reservoir by combining seismic attributes derived from the P-P data and porosity logs. Mapping porosity is extremely important in the development of a hydrocarbon reservoir. In order to achieve my goal, I used the Hampson-Russell software and its linear multi-regression and neural networks geostatistical tools to predict porosity between the seismic attributes and porosity logs at the well locations. After predicting porosity in well locations, the relationships were applied to the attributes and then I was able to generate one 3-D porosity volume. Soubotcheva & Stewart (2004) did similar research predicting density porosity logs and reservoir rocky property, using seismic attributes derived from P-P inversion. They showed that geostatistical methods could be used to predict density porosity (percentage) using inverted seismic attribute as a guide. They also used the Emerge software (Hampson-Russell) to find relationships between seismic and well data. Todorov et al. (1998) using multiregression analysis and neural networks derived a statistical relationship between the sonic velocity and seismic attributes at the well locations. After that, they applied it to the seismic data generating pseudo logs at the trace locations and also a sonic velocity cube, where they were able to interpret sand channels with low velocity anomalies. 5.1 Porosity Logs The well logs that I used in my statistical approach were the density logs (Figure 5.1). The density logs presented in the field measured the bulk density (ρ b ), i. e., the density of the entire formation. In order to determine density porosity, the matrix density and type 58

76 of the fluid in the formation must be know. Following Asquith et al. (2004) the formula for calculating density derived porosity (φ D ) is : Figure 5.1: From left to right, wells 01-D, 02-D, 11 and 17, showing the density logs that were used to compute the porosity logs. The main reservoir is highlighted in all wells in the orange box. φ D = ρ ma ρ b ρ ma ρ fl (5.1) where, ρ ma is the matrix density and ρ fl is the fluid density. In my calculations, I used the standard value for sandstone ρ ma = 2.64g/cm 3 and the fluid density value from the field ρ fl = 0.882g/cm 3 (29 o API). In addition, it is well known in the petrophysics literature (Asquith et al., 2004) that the combination of density and neutron measurements is the most widely used porosity log combination. Neutron logs are porosity logs that measure the hydrogen concentration in a 59

77 formation. Figure 5.2 depicts the crossplot between density logs and neutron logs at wells 01-D, 02-D and 11, with the color representing the gamma-ray measurements. Superimposed on the plot (as an overlay) is a pure sandstone lithology line, where the porosity is indicated along the line. The correlation between the reservoir points (in red) and the sandstone line is very good. After this analyses, I was able to use my density logs as input for my porosity logs calculations. Figure 5.2: Neutron-density crossplot at wells 01-D, 02-D and 11. The colors of data points are given by gamma-ray logs. Overlaying the plot is a pure sandstone lithology line, where the porosity is indicated along the line. The red reservoir points has an excellent correlations not only with the sandstone line, but also with the porosity lines. I used 4 wells for which density logs exist and which are located in both main reservoirs in the field. Using the formula above to convert density logs to density porosity logs, 4 calculated porosity logs loaded into Emerge software (Figure 5.3). The same window analyses 60

78 will contain an extracted seismic trace at the well location. To improve the prediction, I use the angle gather traces, as an external attribute. I used a seismic trace from my inverted elastic impedance as an initial input. Figure 5.3: From left to right, wells 01-D, 02-D, 11 and 17, showing the computed porosity logs (red) and the inverted P-impedance (blue) extracted at those well locations. The main reservoir is highlighted in all wells in the orange box. 5.2 Porosity Prediction The geostatiscal approach used in order to predict porosity from inverted volumes is summarized in the flow-chart shown in the Figure modified from Soubotcheva & Stewart 61

79 (2004). Figure 5.4: Data-driven statistical interpretation - modify from Soubotcheva & Stewart (2004). Currently, there are several methods available in the industry using multiattribute transforms that have been used for estimation of porosity in the reservoir layers. I started my prediction using the multiattribute transforms, that use the covariance matrix to predict the parameter from a linearly weighted sum of the input seismic attributes. In general, the 62

80 relationship (in the time domain) between the log property (porosity logs) and the seismic attributes (from inverted volumes) can be written in the following form (Hampson et al., 2001): P (x, y, t) = F [A 1 (x, y, t), A 2 (x, y, t),..., A M (x, y, t)] (5.2) where P (x, y, t) is the porosity log as a function of coordinates x, y, t, F [...] is the functional relationship, and Ai, i = 1,..., M, denotes the seismic attributes. A number of seismic attributes (more than 20) are extracted from the inverted volumes. Then I qualified the seismic attributes according to their linear correlation with the predicted porosity. These relationships can be found using linear multi-regression analysis, provided by the EMERGE software from Hampson-Russell. For N measured time samples from the logs (converted from depth to time), we have: φ(t) = W 1 A 1t + W 2 A 2t W M A Mt + W M+1 (5.3) where t = 1,..., N represents the time samples, and W i, i = 1,..., M + 1 are weights determined by least-squares optimization. After performing the multivariate linear regression analysis (Figure 5.5), 7 attributes showed the highest correlation (greater than.25) with porosity logs : amplitude weight cosine phase, cosine instantaneous phase, filter 5/10 15/20, inverted P-impedance, filter 15/20 25/30, dominant frequency and Y-coordinate. In order to determine how many attributes I have to choose, Emerge software divides the entire dataset into two groups (Figure 5.6): a training dataset (original wells, in black) and a validation dataset (predicted data, in red). The horizontal axis shows the number of attributes used in the prediction. The vertical axis is the root-mean-square prediction error for that number of attributes. I have chosen 7 attributes to use, and an operator length of 5 samples. From this graphic, I conclude that the ideal attribute number to use is 5 attributes. Using 5 attributes allowed me to avoid a greater error in porosity prediction. 63

81 Figure 5.5: The results of step-wise linear regression applied to porosity log estimation problem. The first 7 attributes showed the highest correlation with the porosity logs. The most valuable attributes are shown in Figure 5.7. Barnes (2007) describes that the cosine instantaneous phase attribute acts like an automatic gain control (AGC) without amplitude information, and its applied to reveal detail and reflection continuity. On the other hand, the instantaneous amplitude attribute is a measure of reflection strength (independent of polarity and apparent phase) and it s primarily used to distinguish bright events. It s known that the frequency content of the porosity log is typically much higher than that of the seismic attribute. Sample-by-sample analysis between porosity and seismic attributes may not be optimal. To avoid this frequency problem, the Emerge program assumes that 64

82 Figure 5.6: Prediction error plot, where the horizontal axis shows the number of attributes used in the prediction. The vertical axis is the root-mean-square prediction error for that number of attributes. Original wells are in black and validation dataset (predicted data) are in red. each sample of the porosity log is related to a group of neighboring samples on the seismic attribute, using a convolutional operator: φ = W 1 A 1 W 2 A 2 W 3 A 3 W 4 A 4 W 5 A 5 (5.4) Therefore, a multiattribute transform using 5 attributes was selected for application. A crossplot of actual and predicted porosity values using points from the analysis windows at 4 selected wells is shown in Figure 5.8. The correlation coefficient for the linear regression using 5 attributes is 0.65, with an average error of A tendency to over-predict the lower actual porosities and to under-predict the higher porosity values is shown in the crossplot derived from the multiattribute transform model using step-wise linear regression. 65

83 Figure 5.7: From left to right, porosity logs and the most valuable attributes: amplitude weight cosine phase, cosine instantaneous phase, filter 5/10 15/20, filter 15/20 25/30 and inverted P-impedance at well-11. Each sample of porosity log is related to a group of neighboring samples on the seismic attributes. Figure 5.9 shows the comparison of actual and predicted porosity logs at the 4 selected well locations after application of multiattribute transforms using 5 attributes. It is clear that the multilinear regression method modeled the porosity curve, but it failed to pick the extreme values in the predicted curve. Finally, I used the Probabilistic Neural Network (PNN) technique. PNN is a non-linear mathematical interpolation scheme (Hampson et al., 2001), similar to the kriging interpolating technique, that I used in order to increase both the predictive power and resolution of derived porosity volume. The estimation is based on the fundamental equation of the 66

84 Figure 5.8: Crossplot of actual and predicted porosity using multiattribute transforms. Data points from analysis zone of each well are shown in one color. (general regression) PNN: where y (x) = n [y i exp( D(x, x i )] i=1 n [exp( D(x, x i ))] i=1 (5.5) and y D(x, x j ) = p [ ] 2 (xj x ij ) (5.6) σ j=1 i is the unknown dependent variable (porosity) and x 1, x 2,..., x p are the input independent variables. D(x, x j ) is, in other words, the distance between the predicted point, x, and the training points x i, scaled by the smoother parameter σ i. In general, the use of PNN s can be divided into four steps (all of these performed in the Hampson-Russell software): 67

85 Perform step-wise multilinear regression and its validation (as describe above); Train neural networks to perform the nonlinear relationships between seismic attributes and porosity at well locations; Apply trained neural networks to the entire 3D seismic volume; Validate results on wells withheld from training. The PNN was trained to predict the porosity taking advantage from the multiattribute step-wise regression results performed and validated before. Non-linear relationships between seismic attributes and rock properties will be trained by the PNNs. Using the same 5 seismic attributes that were selected from the multiattribute step-wise regression, I trained the PNN method. Figure 5.10 shows the crossplot of predicted porosity values against actual porosity values using points from the analysis of four wells. This PNN technique provided a better correlation coefficient of 0.93 between actual and predicted porosity with an average error of After the PNN, the predicted porosity logs are very close to the actual logs in the reservoir zone, as shown in the Figure The validation process is the last step of the PNN technique. Using the background achieved with the previous 4 wells, the PPN was training in a blind well, an well that was not involved in the training process. Figure 5.12 shows the well-13 and its validation results. PNN technique was able to predict the porosity with a 0.59 cross-correlation coefficient, that is a good result. Finally, using the PNN model I created a porosity volume that conformed to the log-based geological model and the inverted elastic impedance. Figure 5.13 represents an arbitrary line crossing three wells in the predicted porosity volume. In this section, I m showing the blind wells 15 and 18 and its excellent correlation with the predicted porosity results. From this volume I generated a porosity map 30 ms below the reservoir, as shown in the Figure It is clear to see that the predicted porosity map shows an excellent correlation with both main reservoir areas. 68

86 Figure 5.9: Application of multiattribute transforms using 5 attributes to predict the porosity. Low-frequency trends are adequately predicted, but the transform fails to predict the extreme values. Correlation is valid only in the analysis window. 69

87 Figure 5.10: Crossplot of actual and predicted porosity using probabilistic neural networks for 5 attributes. Data points from analysis zone of each well are shown in one color. From PNN s prediction, data points are very close to the regression line and scatters are minimum. 70

88 Figure 5.11: Application of PNN using 5 attributes to predict porosity. Extreme values are predicted with higher accuracy. Correlation is valid only in the analysis window. 71

89 Figure 5.12: Validation results for PNN at well-13, using 5 attributes to predict porosity. The cross-correlation coefficient is 0.60 between the original logs (black) and the predicted porosity log (red). The main reservoir is highlighted between orange lines. 72

90 Figure 5.13: NW-SE arbitrary line from the predicted porosity volume generated based on linear multiattribute step-wise regression and PNN method using 5 attributes. The top of reservoir is the dotted black line and the blue logs are the original porosity logs at wells 15, 11, and 18 from left to right. Blind wells 15 and 18 shown an excellent correlation with the predicted porosity volume. 73

91 Figure 5.14: Porosity map, extracted 30 ms below the reservoir, generated based on linear multiattribute stepwise regression and PNN method using 5 attributes. The map shows an excellent correlation with both main reservoir areas. The dash line contours represents both main reservoir in the field. 74

92 CHAPTER 6 RESERVOIR CHARACTERIZATION Using the pre-stack inversion results and the porosity prediction, I move now to reservoir characterization. In this analysis I will use the main results achieved in my research: Inverted elastic impedance from pre-stack P-P angle gather inversion (EI); Inverted shear impedance from pre-stack P-S angle gather inversion (SI); Attribute volume from the difference between the elastic and shear inverted impedance volumes (EI SI); The predicted porosity volume, generated using linear multi-regression and neural network geostatistical tools between the inverted seismic attributes and porosity logs at the well locations. As described by Voelcker et al. (2000) and Ribeiro (2011) the turbidite reservoir consists of two zones: the upper zone, that has widespread lateral distribution, and the lower zone, that is thicker, more restricted in areal extent and is associated with confined channelized deposits. The upper zone is composed of unconfined channel deposits and fans. The upper zone is relatively easy to interpret in the seismic data, because the contrast between the shale above it, giving rise to a large negative amplitude at the top of sand interface. On the other hand, the lower zone is not so clearly defined in the seismic data, mainly because it is composed of canyon-fill and mass transport deposits. From the inverted EI volume it is possible to separate the two reservoir zones. Figure 6.1 shows an EI attribute map extracted from a 30 ms window from the top the sandstone, where the reservoir s upper zone is highlighted. The main reservoir is characterized by channel system deposits highlighted by black-dashed lines. A divergent channel system is 75

93 highlighted by black-rounded-dots. Figure 6.2 also shows an EI attribute map, but this time extracted in a 40 ms window below the reservoir plus a 20ms constant time interval, where now the reservoir s lower zone is highlighted (black-rounded-dot lines). Two seismic sections are shown in Figure 6.3 and Figure 6.4, where both the upper and lower reservoirs are interpreted in the EI volume. The EI volume has been used to successfully separate the upper and lower reservoir zone. Figure 6.1: EI minimum attribute map extracted over a window 30 ms below the top of reservoir. The upper zone reservoir is highlighted in black-dash lines and represents the main reservoir. The divergent channel reservoir is highlighted by black-rounded-dots, representing the E-W divergent channel system. From the SI I was able to identify the main faults that controlled the reservoir deposition. Figure 6.5 shows an SI attribute map extracted 30 ms over a window from the top the sandstone, where the main reservoir structure, inherited from the carbonate platform, is shown in the black dashed lines. From this map, I was able to distinguish the structure 76

94 Figure 6.2: EI minimum attribute map extracted in a 40 ms window below the reservoir plus a 20ms constant time interval. The reservoir s lower zone is highlighted by black-roundeddots. that controls the lower reservoir zone, highlighted in the dashed blue lines. Ribeiro (2011) showed this same structural trend by mapping the difference in two-way-time between the top of the carbonate and the top of the reservoir. From the EI SI volume, I was able to interpret a Direct Hydrocarbon Indicator (DHI). As the shear impedance is not sensitive to hydrocarbon presence in the reservoir, and the EI volume had a good correlation with both main reservoirs, the EI SI volume worked as a DHI attribute. Figure 6.6 shows an EI SI minimum attribute map extracted 60 ms below the reservoir, where red colors indicating the smallest values are correlated with hydrocarbon presence. I used a 60 ms window to detect not only the upper zone, but also the lower reservoir zone. 77

95 Figure 6.3: Inline 1391, showing the EI volume. The reservoir s upper zone is highlighted by black-dashed lines and the reservoir s lower zone is highlighted by the black-rounded-dots. The red-dark lines are indicating the main faults present in the field. The predicted porosity volume also shows good correlation with both upper and lower reservoir zones. Figure 6.7 depicts a porosity map extracted 30 ms below the top of reservoir interpreted in the seismic section. It clearly distinguishes the reservoir within a porosity range from 20 to 28 percent, shown in hot colors. Figure 6.8 shows an inline section of the porosity volume. I use the porosity volume to interpret the main zones in the reservoir: the upper zone (red-dashed lines) and the lower zone, highlighted by the black-dashed lines. Another important feature highlighted in the porosity map is the possible satellite reservoir towards south shown by the white line anomaly. The same feature is also present in the EI and EI SI maps. Finally, I integrated the results, cross-plotting the inverted elastic impedance (EI), the attribute EI SI and the predicted porosity volume to characterize the reservoir. Figure

96 Figure 6.4: SW-NE arbitrary line crossing the main reservoir, showing the EI volume, gamma-ray logs (blue) and P-wave logs (red). The reservoir s upper zone is highlighted by black-dashed lines and the reservoir s lower zone is highlighted by the black-rounded-dots. Both canyon-filling deposits and mass transport deposits from the lower zone are interpreted in the EI volume. shows a cross-plot from predicted porosity and inverted EI SI from the main reservoir area, showing that is possible to distinguish good quality reservoir facies (highlighted in yellow) from poor quality reservoir zones (highlighted in orange). Figure 6.10 is a inline section extracted 100 ms below the top of reservoir, where it is possible to distinguished high porosity facies from poor porosity facies. These analyses were extracted from the previous crossplot. Figure 6.11 shows that from the inverted volumes and porosity prediction the two main porous facies can be also distinguished in the E-W divergent channel reservoir: the good reservoir quality facies (highlighted in the red circle) and the poor reservoir quality facies (highlighted in the yellow square). In summary, cross-plotting all three volumes as shown in Figure 6.12, the reservoir is represented by the smallest values from EI and EI SI and by the highest porosity values. 79

97 Figure 6.5: SI minimum attribute map extracted in a 60 ms window below the reservoir. Note that the main faults studied in the research area have an excellent correlation with SI attribute (black dashed lines). The reservoir s lower zone has its thicker and lesser porous compartment between the highlighted blue-dashed lines. 80

98 Figure 6.6: EI SI minimum attribute map extracted within 60 ms window below the reservoir. The both main reservoir s limits are highlighted in black-dashed lines. 81

99 Figure 6.7: Porosity maximum attribute map extracted in a window 30 ms below the top reservoir. The main reservoir is highlighted in red-dash lines. The E-W reservoir is highlighted in red-round-dot lines. Note the southern anomaly highlighted by the black-dash line as a possible field extension. 82

100 Figure 6.8: Inline 1393, showing the predicted porosity volume. Porosity values ranging from 0.12 (purple) to 0.28 (dark-red). The reservoir s upper zone has its top at the yellow line and its bottom interpreted at the red-dashed lines. The lower zone is highlighted in the black-dashed lines. 83

101 Figure 6.9: Predicted porosity versus inverted EI SI crossplot from the main reservoir, where data points represent the elastic impedance shown in the color bar. Good quality reservoir facies are highlighted in yellow, and poor quality reservoir facies are highlighted in orange. 84

102 Figure 6.10: Inline section 1391 extracted 100 ms below the top of reservoir, showing the high porosity facies (yellow) and the poor porosity facies (orange). In the upper right corner is shown the porosity map extracted 30 ms below the reservoir and the location of the inline section (black line). 85

103 Figure 6.11: Predicted porosity versus inverted EI SI crossplot from the E-W divergent channel reservoir, showing good quality reservoir facies (highlighted in the red circle) and poor reservoir quality facies (highlighted in the yellow square). 86

104 Figure 6.12: EI SI versus EI where data points represent the predicted porosity shown in the color bar, values ranging from 0.12 (purple) to 0.28 (dark-red). The reservoir is highlighted in the red ellipse, combining the smallest values from EI and EI SI and the highest porosity values. 87