PDF- -Seismic Interpretation - Offshore Energy Research Association - Calibration of Seismic and Well Data

Description

CALIBRATION OF SEISMIC AND WELL DATA Towards Improved Quantitative Seismic Reservoir Characterisation of the Triassic to Middle-Jurassic Gullfaks Reservoir Units of the northern North Sea

Isaac Bisaso

Density lo g × V e l'o c'i t y l'o g = Impedance (Z)  Reflectivity series,

* Wavelet,

convolution model) behind constucting a synthetic seismic trace from a density and sonic (velocity) log

Master of Science Thesis Discipline: Petroleum Geophysics

Department of Earth Science University of Bergen December,

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

Project Supervisors: Prof

Tor Arne Johansen (UiB) Eng

Bent Ole Ruud (UiB)

E-mail: [email protected]

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

Abstract Characterization and evaluation of (oil and gas) reservoirs is typically achieved using a combination of seismic and well data

It is therefore critical that the two data types are well calibrated to correct and account for the fact that seismic data are measured at a scale of tens of meters while well data at a scale of tens of centimeters

In addition,

seismic data can be poorly processed

some well logs can be damaged,

affected by mud filtrate invasion or completely missing

This research addresses the methods of (1) editing,

conditioning and petrophysical analysis of well logs and (2) joint calibration of seismic and well data to improve correlation and consistency between the two data types

A case study using a data set from the Gullfaks filed is presented

this field is in tail production and therefore improved seismic reservoir characterization to prolong its production life is quite essential

With the help of Geoview,

Elog and AVO modules of Hampson-Russell software and Geovation/Geocluster software

petrophysical modeling and analysis,

and joint-calibration of the data were carried out

The results show that locally calibrated rock physics models (of for instance Gardner’s and Castagna’s equations) produce more accurate synthetic well logs (of missing or damaged curves) than those produced using ‘Global’ relations

Fluid replacement modeling was carried out to factor in the presence of hydrocarbons in the reservoir zones

the results show more accurate prediction of well logs in the reservoir zones

The quality of well logs was greatly enhanced,

in preparation for the joint calibration process

Multi-well wavelet extraction and analysis was done to extract a single wavelet

the wavelet so extracted produced synthetic data that correlates well at all well locations

In some of the wells the correlation coefficient was over 0

In one of the wells the correlation coefficient rose from

The study demonstrates that it is possible to obtain a high correlation between seismic and well data,

if the data are well processed and conditioned

Multi-well wavelet extraction produces a wavelet that is applicable at all well locations

Keywords: Rock physics,

Fluid Substitution,

Seismic and well logs,

Wavelet extraction,

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

Table of Contents Abstract

Strategy and Prognosis

editing and analysis of well logs

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

0 Summary,

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

List of tables Table 1: Original well log data for the three wells used in this project

In the text the wells are referred to by the their ‘short name’

adapted from Hampson-Russell (2004),

adapted from Hampson-Russell (2004),

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

Acknowledgments I am highly indebted to Prof

Tor Arne Johansen and Eng

Bent Ole Ruud under whose supervision and guidance this research was undertaken

The duo is credited for their selfless support throughout my period of study at the Earth Science Institute in Bergen

Johansen,

accepted me at a time when he knew nothing of my abilities and inabilities

why did you have to take such a gamble

Bent spent a lot of time teaching me how to use Software packages and offered helpful suggestions and guidance on the results presented herein

I thank Dr

John Mary Kiberu (Makerere University) for the helpful guidance on the layout of the report and for reading through and making constructive comments on the presentation style

I am very grateful to my colleagues: Kenneth Bredesen and Cathrine Eide (“Princess Kate”) for the love,

care and support that kept me reading and writing

The duo read through and made useful comments unto this thesis

I thank my family and friends who have had to bear with my long period of absence,

amidst all the misfortunes that befell the family

Statoil and CGGVeritas are acknowledged for providing the data set and software tools,

My entire Masters education,

would not have been undertaken without the generous financial support from the government of the Royal Kingdom of Norway

may God unreservedly bless the Norwegian people

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

Dedication: 1

In Loving Memory of my beloved Grandmother and Grandfather who rested while I was in a land so far away in pursuit of knowledge‡

Rest in Peace

Mum and Dad – above all beings on earth,

Though it cost all you have,

” – Proverbs 4: 7 (NIV) viii

Petroleum Geophysics

Isaac Bisaso,

University of Bergen

List of Acronyms AVA: Amplitude Variation with Azimuth AVI: Amplitude Variation with Incidence angle AVO: Amplitude Variation with Offset EOR: Enhanced Oil Recovery GC: Gas Chromatography GOR: Gas Oil Ratio IOR: Improved Oil Recovery MS: Mass Spectrometry NGL = Natural Gas Liquids NORSAR: Norwegian Seismic Array NPD: Norwegian Petroleum Directorate OWC: Oil Water Contact scm = standard cubic meters TOC: Total Organic Carbon TWT: Two Way Time UiB: Universitetet i Bergen (= University of Bergen) VSP: Vertical Seismic Profiling RSI: Rock Solid Images

Isaac Bisaso,

Petroleum Geophysics

Chapter 1: Introduction

who (with limited or no access to well data) cannot always tie the seismic data and its character (attributes) to properties of the formation as evidenced from the well data

Simply put,

while the former works with un-calibrated seismic data the latter works with calibrated seismic data

And the increasing use of 3D seismic data for quantitative reservoir characterization arouses the crucial importance of a reliable well to seismic calibration,

(Nathalie and Pierre,

Unfortunately,

there has not been a case (so far) where seismic data and well data perfectly and out rightly correlate without some ‘intelligent’ processing and ‘shifting’ schemes,

those presented by Roy and Tlanyue (1998)

Well data (of all types) are measured to the order of tens of centimeters while seismic data samples lithologies at a scale of the order of tens of meters

This calls for advanced mathematical processing of well data to upscale it so that it may be compared with seismic data

Well data are not always recorded all the way up the borehole,

and it can be poorly recorded,

The data can be greatly erroneous in damaged well bore sections and highly porous zones (due to mud filtrate invasion)

These are a few among the many issues that have been examined in this study

to develop a framework which can help reservoir geophysicists (geologists and engineers) to take full advantage of the availability of both well and seismic data

Seismic data is in itself based on assumption of noise free zero-offset traces (convolution model),

yet it is constructed from non-zero offset traces that have to be rid of multiples and migration artifacts and other forms of noise

This falls in the domain of seismic data processing,

is too broad a topic to be addressed alongside the present research

Because of the breadth of the topic of seismic data processing,

this research uses a presumably well processed seismic data set

However,

limited post stack processing was carried out to condition the data for this research (e

‘windowing’ (reducing the volume of) the seismic data to cut out unwanted parts

Regarding the actual integration of well and seismic data,

a number of approaches are studied,

for instance: using VSP (Vertical Seismic Profiling) data and seismic forward modeling (computing synthetic data from well logs)

A more ‘hybrid’ tool published by Linari et al (2004) is to ‘invert’ the seismic data for velocity and directly compare the inversion result to sonic data from wells

All these methods are considered in this research

Isaac Bisaso,

Petroleum Geophysics

Chapter 1: Introduction

The single most important link between seismic and well data within the context of forward seismic modeling (a key topic in this research) is wavelet extraction

A detailed multi-well wavelet extraction scheme has been developed to increase the likelihood of achieving a high correlation and consistency between seismic and well data

the objective of this research is to develop a framework under which geologists,

geophysicists and engineers can improve the integration of seismic data and well data (like never before)

This involves intentionally ‘deleting’ some of the details in well logs (called upscaling),

simulating high quality synthetic seismic data from the well logs and integrating them with real seismic data

Special attention is paid on ensuring that the well logs are as close to the ‘ground’ truth as possible

Much emphasis is given to wavelet extraction a key link in seismic to well calibration

The work flow involves the following steps: 

condition and upscale well logs,

synthesize missing or damaged logs

Editing and conditioning to remove spikes,

the effects of mud-filtrate invasion and formation damage

Upscale the well logs to remove details which are much loved by geologists but are a nuisance to calibration and integration of seismic and well data

Synthesis of missing and damaged logs by petrophysical modelling to make a complete suite of logs necessary for subsequent reservoir studies

Assess the suitability of traditional methods as well as recently developed methods of seismic to well data calibration

One of the key methods examined is the Forward seismic modelling method,

regarding which a detailed multi-well wavelet extraction technique is developed

Investigate how well calibrated seismic and well data improves seismic reservoir characterisation

A brief description of the methodology involved in implementing the steps above is discussed in the next section

The detailed methodology is presented in proceeding chapters

The Gullfaks field which is located in the northern North Sea (fig

a famous and mature oil province,

provides a rich data set of well and seismic data for a study of this kind

The field has been produced since 1986 from three platforms

A detailed description of the study area is given in chapter 2

Isaac Bisaso,

Petroleum Geophysics

Chapter 1: Introduction

GULLFAKS OILFIELD NORTH SEA

Figure 1

modified from Norwegian Petroleum Directorate,

NPD (2010)

Statoil (the operator of Gullfaks field) generously provided the following well and seismic data set for this project: 1

Seismic dataset 3D seismic data (size: 3

mid (15-25 degrees) and far (25-35 degrees) partial angle stacks

Isaac Bisaso,

Petroleum Geophysics

Chapter 1: Introduction

Five vintages (1985,

The vintages acquired after 1985 have shadow zones around the oil platforms and other infrastructure in the Gullfaks area (fig

In this study we used the base line data (1985) to avoid the shadow zone

Also all the wells used in this study were drilled prior to the onset of production

as such there was no need to incorporate production history

Figure 1

the red rectangle encloses the ‘shadow’ zone where the production platform is located

Modified from data provided by Statoil

Seven (7) Interpreted Seismic horizons The following interpreted horizons were provided in depth and time domains:  BCU (Base Cretaceous Unconformity)  Top Tarbet  Top Ness  Top Ness 2A  Top Broom  Top Cook  Top Stratfjord A detailed description of these stratigraphic units/surfaces is given in Chapter 2

In this research,

we did not concentrate on a particular reservoir

Chapter 1: Introduction

Isaac Bisaso,

Petroleum Geophysics

used in understanding the stratigraphy of the area and in identifying the area extent of reservoir units in between the wells studied

Well data There are nine wells in the area

two of which are outside the fully migrated seismic cube

Of the remaining seven wells,

one is within the ‘shadow’ zone (see figure 1

This leaves only three ‘usable’ wells,

within the scope of this study

The well data that for the three wells are summarized in table 1 below: Table 1: Original well log data for the three wells used in this project

In the text the wells are referred to by their ‘short name’

Official

Depth range (of

Well name

34/10-4

34/10-11

34/10-14

Well 11

Well 14

Original log curves provided

180-2799

Gamma ray: 1532-2465 m Caliper: 1532-2465 m Deep Resistivity: 1532-2465 m Neutron porosity: 1532-2465 m Bulk density: 1532-2465 m Compressional velocity: 1758-2465 m No shear velocity

1798-2155

Gamma ray: 1798-2154 m Caliper: 1798-2155 m Deep Resistivity: 1800

1719-2647

Gamma ray: 1719-2647 m Caliper: 1800-2647 m Deep Resistivity: 1900

In addition to the original curves shown in the table above,

we also received a suite of well logs that were edited,

processed or synthesized by Rock Solid Images (RSI) using their commercial Geophysical Well Log AnalysisTM tool

Some of the RSI curves were used to benchmark our own results

Isaac Bisaso,

Petroleum Geophysics

Chapter 1: Introduction

As can be noted from table one above,

one of the inherent problems of most well data is incompleteness: some logs exist only in the logged sections while others are completely missing

The solution is to turn to petrophysical modelling and synthesis using empirical models as addressed in chapter 3 of the thesis

In the same chapter we discuss other processing flows necessary to condition well data in preparation for integration with seismic (and other data types)

Data analysis and interpretation were done using Hampson-Russell software packages from CGGVeritas

Well data treatment and analysis were done using Geoview and Elog modules

Elog module was particularly used for editing,

conditioning and petrophysical modelling (Fluid replacement modelling and synthesis of missing logs)

For the joint calibration of seismic and well data (wavelet extraction,

synthetic modelling and multiwall analysis),

Basic post-stack seismic processing (for instance windowing the seismic data) was carried out using Geovation/Geocluster processing suite from CGGVeritas

Geopad and Team view modules were used for ‘job building’,

database management and visualisation,

A detailed description of methods and specific processing schemes is given in the relevant chapters

this was done to keep the results and discussion closer to the methods so that the material flows well

The chapter also gives an overview of the petroleum system,

production history and prognosis of the field based on published data

Chapter 3 is dedicated to editing,

calibration and petrophysical analysis of well data

Issues such as synthesis of missing logs,

repair of damaged logs are all addressed in this chapter

A special section within chapter three is dedicated to fluid substitution modelling as applied to correcting for mud filtrate invasion and incorporating production history (in case of 4D seismic data)

The ‘core’ part of the research,

the integration of seismic and well data in the context of joint calibration is given in Chapter 4

Special sections on wavelet extraction and multiwell analysis of wavelets are included in this chapter

Isaac Bisaso,

Petroleum Geophysics

Chapter 1: Introduction

Chapter 5 provides a summary of the entire work

the major conclusions and contributions thereof

Limitations and recommendations for future work are also given in the same chapter

masters and PhD theses that have been published covering some of the themes under this research

Unique to the presentation style of this work,

the author chose to review the previous work at the beginning of the relevant sections/chapters throughout the thesis

This should help the reader to compare the results of previous workers to those of the current researcher

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

It is situated in the shallowest structural element of the Tampen spur area,

in the central part of the East Shetland Basin,

on the western flank (side) of the Viking Graben (Fossen et al,

It lies in the Norwegian sector of the northern North Sea,

NNE-SSW-trending fault block system (Fossen 1998,

The water depth in the area is 130 – 220 metres

Figure 2

Modified from Hesjedal,

(nd) and Norwegian Petroleum Directorate,

NPD (2010) 8

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

The field was discovered in 1978 by exploration well 34/10-1 (spudded 20 June 1978)

The exploration well was based on interpretation of an earlier 2D seismic data set that delineated a pre-cretaceous structural high in the north-eastern part of block 34/10

By the end of the initial exploration period (with at least 10 discovery wells),

it was clear that the field covers the entire eastern half of the 10-25 km wide Gullfaks fault block with an area extent of up to 55 km2

This made it clear that at least three platforms were needed to fully exploit the reserves (fig

The field has three integrated processing,

drilling and accommodation facilities with concrete bases and steel topsides (Gullfaks A,

B and C)

Gullfaks A (on stream since 22nd December,

while Gullfaks B (on stream since 29th February,

Gullfaks A and C also receive and process oil and gas from the neighbouring Gullfaks Sør,

Gimle and other satellites (Statoil,

Figure 2

encircled in red is the study area

Modified from NPD (2010)

Its formation has also been linked to the Caledonian

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

When the European,

Greenland,

and North American plates begun to separate,

a triple-rift junction developed somewhere to the northeast of Scotland

two of its arms opened up forming the Norwegian Sea and the Atlantic Ocean

The southeastern branch of the triple-R junction subsided but failed to open,

providing the present day North Sea oil province (Selley,

In this respect therefore,

the North Sea is an Aulacogen (failed rift) basin

Subsequently,

it is believed to have experienced at least two main rifting episodes: 

First is the older Permo-Triassic rifting episode which is prominent on regional seismic data

It Shows up as the larger N-S to NNE-SSW faults,

reflecting the overall E-W extension across the rift

This rifting created the Viking Graben and a series of westerly dipping fault blocks (which is characteristic of the present day Gullfaks field) and half Grabens in the eastern East Shetland Basin

The second major rifting episode occurred during Middle Jurassic to early Cretaceous times

This suite of younger roughly E-W trending faults are very visible in local seismic vintages and it is where majority of the hydrocarbon bearing formations within the northern North Sea are located

Of course,

this gives an idea about the timing of hydrocarbon migration

Most of the faults terminate against a regional base cretaceous unconformity which separates the faulted and rotated Triassic and Lower-Middle Jurassic sediments from mainly unfaulted and flat-lying Cretaceous and younger deposits

In fact it acts as the seal for the Gullfaks reservoirs

This unconformity represents a time gap of up to 100 Ma on structurally high areas like the Gullfaks Field (Fossen,

The post-Jurassic history of the North Sea is characterized by basin subsidence and continuous sedimentation

The Gullfaks Field is the most complex structure so far developed in Norwegian waters

This complexity is such that the Gullfaks reservoirs are located in rotated fault blocks in the west and in a structural horst in the east,

with a highly faulted E-W transitional zone in-between (fig

A western domino system comprising of a series of typical domino-style rotated fault blocks

In this compartment,

the normal faults strike N-S dipping to the east at rather low angle of 30-40o while the formations are west dipping (10-15o)

This is a peculiar combination of dips because with a fault dip of 30-40o one would expect the bedding dip in the order of 20-300

Previous workers (for instance Petterson et al,

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

blocks combined with rotational deformation of the blocks and (2) an internal shearing within the separate blocks

An eastern horst structure,

where the Stratfjord formation is uplifted approximately 300m compared to the central area (Petterson et al,

This elevation of the subhorizontal layers and the steepness of faults exposed and led to erosion of a great part of the upper Formations (Brent and cook reservoirs) as seen in figure 2

Between the western and eastern regions is a transitional accommodation zone which could by itself be regarded as a Graben feature

These E-W faults separate the two domains of contrasting dips

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

Horst complex and accommodation zone,

modified from Fossen and Hesthammer,

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

Modified after Petterson,

They were un-conformably deposited on Caledonian basement

The Lunde Formation attracts less attention because its reservoir quality is generally poor

Lomvi Formation has no reservoir potential

Most of the oil in the Gullfaks field is primarily recovered from three early to middle Jurassic age sandstone reservoir units (fig

3 and 2

4): 

The Statfjord Formation

The Cook Formation of the Dunlin group

The Brent Group

Growth faulting occurred during deposition of these reservoir rocks along some fault trends,

probably or at least partly due to differential compaction of Triassic rocks (Goff,

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

This Formation represents an environment that changed its character from a well drained semi-arid setting with episodic sheet flood deposition to a more humid alluvial plain setting

The Stratfjord Formation is subdivided into the Raude,

Eiriksson and Nansen members (fig

A detailed description of these smaller subdivisions has been well outlined by a number of previous workers see for instance Petterson et al

The reservoir quality within the Stratford Formation ranges from very good to poor potential

The others are: (1) Amundsen (Sinemurian-Toarcian marine clay- and siltstones),

The Cook Formation is further subdivided into Cook-1 (a marine silty claystone with zero reservoir potential),

Cook-2 (consisting of bioturbated muddy sandstones with poor to moderate reservoir potential) and Cook-3 (consisting of interbedded sand and shale with good reservoir potential) units

The deposits consist of a sequence of sandstones,

The Brent group was deposited in a deltaic environment

The Brent group is subdivided into five major stratigraphic units: Broom,

Rannoch,

Ness and Tarbert Formations (figure 2

In terms of in-place volumes,

the Brent group is the most important of all the reservoir units

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

RESERVOIR DESCRIPTION MARINE SHALE

RETREATING DELTA FRONT  MOUTH BAR COMPLEX  VERY GOOD LATERAL CONTINUITY  POOR SAND STRENGTH VERY GOOD RESERVOIR DELTA PLAIN  MINOR MOUTH BARS DOMINATE  THIN SAND BODIES  MODERATE CONTINUITY  MODERATE SAND STRENGTH MODERATE RESERVOIR PROGRADING DELTA FRONT  FORESHORE/SHOREFACE COMPLEX  GOOD LATERAL CONTINUITY  MODERATE-GOOD SAND STRENGTH  UPWARD INCREASING PERMEABILITY GOOD RESERVOIR PRODELTA MARINE SHALE ESTUARY/MARGINAL MARINE  HETEROGENEOUS,

GOOD CONTINUITY LOWERRESERVOIR SHOREFACE GOOD  HOMOGENEOUS/BIOTURBATED  VERY GOOD CONTINUITY MODERATE RESERVOIR  OFFSHORE TRANSITION

 MARINE SILTSTONE SHALE MARINE SHALE  MARINE (ESTUARY/TIDAL)

POOR RESERVOIR  MARINE NEARSHORE TRANSITION

FLUVIAL (SEMI-ARID)  GOOD CONTINUITY  DOMINANTLY BRAIDED RIVERS

 FLUVIAL-ALLUVIAL  MODERATE CONTINUITY VERY GOOD RESERVOIR MODERATE-POOR RESERVOIR ALLUVIAL PLAIN (ARID)  LOW SEDIMENT INPUT  MODERATE CONTINUITY

POOR RESERVOIR

Figure 2

Modified after Hesjedal,

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

(Adapted from Petterson et al,

This Formation is often eroded on structural highs in the Gullfaks area but reaches a thickness in the order of 200-400m in the Viking Graben (Petterson et al,

Supplementary source rocks albeit of less importance are assumed to be the shales of the Heather Formation as well as the marine shales of the Toarcian Drake Formation

Other potential source rocks are the shales and coals within the Ness Formation within the Brent group (Petterson et al,

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

There are three important “kitchens” serving the Tampen spur area (fig

the Troll kitchen in the east and the Møre kitchens to the north of Snorre (Petterson et al,

MØRE BASIN

(Modified from Petterson et al,

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

the reservoir quality ranges from poor to very good (fig

The sands were deposited in various environments like: Fluvial-alluvial and Marine environments

A more comprehensive description of the reservoir quality was outlined in section 2

Most faults (but not all) terminate against a major unconformity,

which seals off the reservoirs at approximately 1700m MSL in the crested area (Petterson,

Leakage: hydrocarbon leakage through the cap rock has been reported by a number of previous workers (see for instance Petterson et al 1990 and Larter & Horstad,

It is reported that the leakage is because the integrity of the cap rock is compromised by some fault planes that can be seen crossing the top cretaceous horizon in a few places in the Gullfaks area

This leakage of hydrocarbons from Jurassic reservoirs is supported by the occurrence of minor amounts of hydrocarbons in Paleocene (Tertiary) sands (Petterson et al 1990 and Larter & Horstad,

these gas chimneys above the main reservoirs turn seismic reflectors chaotic in places where they occur due to the geophysical phenomena of “velocity push-down”

fortunately the Gullfaks structure had already been sealed off by the Cretaceous shales and marls

Several workers (e

Two other factors that may be important in primary migration are creation of porosity by conversion of kerogen to oil,

and the reduction in oil/water interfacial tension with increasing temperature (Goff,

Regarding secondary migration,

short distance or direct migration from the nearest ‘kitchen’ in Troll (Viking Graben) might have been impeded by the major east bounding fault

However,

(short distance) migration from spilling structures in the neighboring fields may have contributed to the oil in the Gullfaks

For instance,

Petterson et al (1990) observed that the OWC for Brent Group reservoir in one of the Gullfaks blocks (6 A) is 56m shallower than in the main Brent reservoir,

this coupled with an associated anomalously higher GOR was attributed to oil that migrated up-north from Gullfaks Sør field

Otherwise,

only long distance migration from the other ‘cooking areas’ is

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

Eastward,

tertiary migration beyond the Gullfaks structure,

being the shallowest part of the Tampen Spur area,

through pre-Cretaceous strata is impossible (Petterson et al

the richest source rocks in the study area are the oil prone Kimmeridge Clay (the Draupne formation to be specific to the Gullfaks)

Thus description of source rock geochemistry will be restricted to the Kimmerigde Clay Formation

The immature organic matter of the Kimmeridge Clay (Draupne Formation) consists pre-dominantly of Type II kerogen

Its main macerals are inertinite and vitrinite

The Kimmeridge Clay is rated as an excellent oil source rock,

capable of generating gas at high maturity levels

Its total organic carbon is of the order of 5-10%

This unusually high,

Total Organic Carbon,

TOC (and sapropel) content(s) as observed in the East Shetland Basin may be partly attributed to deposition in restricted fault bounded half grabens

used Vitrinite reflectance versus burial history to estimate the uniform present day maturity gradient

At the present day the Kimmeridge Clay is mature over most of the East Shetland Basin and has reached peak generation throughout the axial region of the basin

The maturity level of the Kimmeridge Clay is close to the oil floor (1

Chapter 2: Understanding the Gullfaks field

GAS WINDOW

Isaac Bisaso,

Petroleum Geophysics

Figure 2

Modified after Goff 1983

Goff 1983,

correlated Hydrocarbon/TOC ratio data for the Kimmeridge Clay with the vitrinite reflectance gradient to determine the vitrinite reflectance level corresponding to peak hydrocarbon generation

The associated oil expulsion efficiency from this source rock is over 20-30%

Organic matter (spore) colouration and source rock electrical resistivity can also be used to estimate source rock maturity

From 2600 to 3200 m,

plant material in the Kimmeridge Clay is light to medium brown,

this according to organic matter (spore) colouration as a maturity indicator,

means that it is moderately mature

between 3200 and 3650 m it is dark brown indicating that it has achieved peak generation (Books and Thusu,

the electrical resistivity (of the Kimmeridge clay) increases from 2-3 ohm metres at 2500-2600 m to a maximum of 10-25 ohm metres at 3500-3600 m

These data indicate that peak generation has occurred between 3200 and 3500 m at a reflectance level of 0

which is consistent with other rock evaluation methods

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

Figure 2

Goff (1983) determined the timing of oil generation from the Kimmeridge Clay from its maturation history using the correlations of vitrinite reflectance with ‘Time Integrated Temperature’† also knows as the maturity index

The areal extent of maturity was then deduced from isopach and structural contour maps of the study area

The study demonstrated that,

oil generation from the Kimmeridge Clay began 70-80Ma ago in the Viking Graben

Peak oil generation was reached 55-65 Ma ago in the Viking Graben

and throughout the Viking Graben

Generation of gas by cracking of oil in the Kimmeridge Clay of the Viking Graben occurred during the last 50Ma

Gas generation from Brent Formation coals began 100 Ma ago in the Viking Graben

peak dry gas generation occurred during the last 40 Ma

Time Integrated Index as a maturation parameter is akin to Lopatin’s Time Temperature Index,

Goff (1983) discussed its mathematical basis

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

Compositional heterogeneities and oil degradation: In the Gullfaks field,

large-scale compositional heterogeneities in petroleum accumulations are well known

These chemical heterogeneities in the petroleum column have been interpreted geochemically by Larter & Horstad,

It seems from these studies that these chemical heterogeneities are beyond the usual aerobic (and anaerobic,

if any) biodegradation and water washing effects,

occurring after the oil has got entrapped

Instead these variations in petroleum column composition may be interpreted as being due to source facies and/or maturity variations in the petroleum charges feeding the oil accumulation

Larter & Horstad,

concentrations of total petroleum,

saturated/aromatic hydrocarbon ratios etc

analysis of whole oil samples with internal standard quantisation

These studies showed that,

while at any location the vertical composition of petroleum is quite constant,

systematic variations in the chemical composition of the petroleum within the Brent Group reservoir are recognized laterally across the field (fig

Degradation is highest for oils in the western and least in the eastern part of the Brent Group reservoir

basically involving selective ‘eating’ of pristane and phytane

This anomaly led to the conclusion that: “the decrease in the absolute amount of n-alkanes across the field is due to biological degradation of petroleum” (Larter & Horstad,

The second anomaly that was revealed by GC/MS analysis results (of Larter & Horstad,

distinguishable petroleum populations exist in the Gullfaks field: one early to mid-mature population present in the Brent Group in the western part of the field,

and a slightly more mature population within the Cook,

Statfjord and Lunde Formations in the eastern part of the field (figure

Larter & Horstad (1992) deduced that the Brent Group reservoir was filled from a related but slightly different source to those filling the Cook/Statfjord Fm reservoirs

But Petterson et al (1990) attributed this anomaly to the fact that hydrocarbons in the Cook and Stratfjord Formations did not suffer from biodegradation as did those in the Brent Formation

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

Strategy and Prognosis Gullfaks field was discovered in 1978 and has been producing since 1986 under production licence PL050,

which was amended into licence PL050B (NPD,

The field is operated by Statoil (70%) in partnership with Petoro AS (30%)

It was originally operated by Statoil in partnership with two other (now defunct) Norwegian oil companies: Norsk Hydro

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

in what would turn out to be the first license ever run by a fully Norwegian joint venture corporation

The original and remaining recoverable reserves (as of 31

NGL = Natural Gas Liquids scm = standard cubic meters

Remaining as of 31

2009 16

the others being: gas injection or water/alternating gas injection (WAG)

The drive mechanism varies between the drainage areas in the field,

but water injection constitutes the main strategy

Status and prognosis: Production from Gullfaks reached its peak in 1994 setting a production record of 605,965 barrels for a single day on 7 October 1994 (fig

Today it can be considered a ‘dying’ field,

it is in tail production phase

The recovery factor on Gullfaks is an impressive 59 per cent

There are continuous efforts being made to increase recovery to at least 62 per cent (fig

partly by locating and draining pockets of remaining oil in water-flooded areas,

and partly through continued massive water injection

The other measures to improve recovery include horizontal and extended-reach wells,

new completion and sand control technology,

and water alternating gas (WAG) injection

Implementation of a chemical flooding pilot is under consideration (NPD,

It is envisaged that if the recovery factor can be increased to around 70 percent then the Gullfaks can live on to up to 2030

It should be noted that in the long run the single most important way of prolonging the production life cycle of a producing platform is not necessarily in squeezing out the already proven reserves (through IOR and EOR interventions),

but through “infrastructure-led exploration which can yield highly commercial finds which can then be brought on stream quickly” (Tom Dreyer)

The most recent example to justify this fact is the August,

2011 

This is a slightly modified statement of Mr

Tom Dreyer,

exploration head for the northern North Sea at Statoil

The original statement was retrieved on 24

Isaac Bisaso,

Petroleum Geophysics

Chapter 2: Understanding the Gullfaks field

discovery of oil in the Gullfaks South area (Rimfaks valley)

These if tied into the existing platforms at Gullfaks can help to keep the production rate above the economic cut off

today the Gullfaks is clearly in tail production

Adapted from NDP (2010)

Gullfaks will live on up to year 2030

But the real future might be in aggressive infrastructure-led exploration to map out pockets of remaining hydrocarbons,

like those that were recently (August,

Modified after,

Eltvik (2011)

Isaac Bisaso,

Petroleum Geophysics

This page was intentionally left blank

infrastructure-led exploration is important and yields highly commercial finds which can be brought on stream quickly,” Tom Dreyer

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

editing and analysis of well logs Reservoir characterization and evaluation are business critical functions in most oil companies

the increased demand of which is driven by economic realities: if reservoirs can be defined better using available technology,

then the end results are higher drilling success and fewer development wells

Better reservoir definition and better reservoir management are the ultimate goals

It requires integration of all available subsurface data but the key data is normally seismic and well data (Vertical Seismic Profiling,

VSP and ‘check-shot’ velocity data)

Each of these data represents measurements,

made using highly sophisticated equipments and highly developed software,

but with a certain level of error

Although the technology involved is ever evolving and improving,

the associated errors need to be properly dealt with,

before the various data can be integrated for reservoir characterization

The manner in which these errors are handled affects the integration of the two data types and determines the quality of the final reservoir model (Jarvis,

For instance,

well logs are sometimes viewed by geophysicists as "hard data" and not subjected to the same level of scrutiny as the “soft” seismic data (see for instance: Nathalie and Pierre (2000),

This can be a mistake because well logs are susceptible to errors from a number of sources (Walls et

In this chapter,

petrophysical control on well logs,

methods and procedures of well log data conditioning are examined using a real well data set,

the integration of the ‘fine tuned’ well data with seismic data is presented in the next chapter

Figure 3

Chapter 3: Petrophysical modelling and analyisis

Isaac Bisaso,

Petroleum Geophysics

Seismic data

Calibration

Well data (Logs,

VSP & check-shots)

QC and Conditioning

Wavelet extraction AVO algorithm & Inversion Engine

Low frequency Model

KEY Input Interpreted horizons

Processing

conditioning and editing The continuous recording of a geophysical parameter along a borehole is called geophysical well logging

when the measured value is continuously plotted against depth,

a geophysical well log is born

Well logs are a result of physical measurement of the earth’s properties taken within the confined space of a borehole (Jarvis,

The probing instruments take the measurements from a very close range to the rocks under in situ (or nearly so) conditions

this is why the well log is the preferred benchmark (“hard data”) in the calibration process

Unfortunately,

these measurements are affected by borehole irregularities (rugosity),

casing points and they very much depend on the elapse of time between drilling and logging of the hole among other factors

Therefore,

the primary goal in processing well log data is to rid the data of measurement related errors and to obtain consistent and accurate logs from well-to-well

In addition,

the logs represent data that is sampled at much higher frequencies (e

This creates a lot of detailed information some of which is outside the seismic resolution

hence some type of dispersion correction (up-scaling) should be applied to account for the differences in frequencies between logging tools and surface seismic reflection before calibrating these data against each another

And quite often the time and/or tools for measuring some parameters are not available,

in such cases there is need to synthesize such logs from other existing logs

The methodology and procedures of accounting for these problems and uncertainties follow

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

well log data often requires some editing,

and interpretation before they can be used in any reservoir characterisation study

The key steps involved in editing and repairing well logs are discussed hereunder

a) Reconciling sonic logs with check-shot data Check-shot (borehole velocity survey) data do not usually tie well with sonic data because of various reasons,

for instance sonic data is highly contaminated by dispersion effects especially in damaged holes,

it is also affected by mud-filtrate invasion effects in porous zones

And unlike sonic data,

check-shot and surface seismic data probe the rock in its undisturbed state

Additionally,

sonic velocities are usually higher than check-shot velocities because of dispersion effects (sonic logging uses higher frequency pulses which travel a lot faster)

Much as check shot data can also suffer from “misfires,

cycle-skips and poor processing flows” (Box and Loren,

they are to be more trusted (than sonic data)

One of the first steps in calibrating well logs is to apply check-shot data

The goal of this step in the well log calibration process is to bring the timing of the sonic log into agreement with the “more accurate” seismic times from a checkshot survey

The theoretical background of check-shot surveys (acquisition and processing) is outside the scope of this study,

it suffices therefore to simply present results of applying check-shot data on our sonic logs

Results and discussion: In figures 3

3 and 3

The result for well 3 is to be expected since the original sonic should be higher than the result after applying check-shot data

The check shot data for well 11 is questionable

how can the sonic data be lower than the check-shot corrected data

? The results for well 14 show that there was little drift between the check-shot and sonic data for this particular well

this is why the resulting curve is just a little higher than the original curve

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

Figure 3

In the first track or panel is the checkshot log,

the blue curve is the resulting P-wave curve after applying check-shot data on the original (blue) P-wave log

The resulting sonic log has lower values since the check shot data is lower than sonic data as expected

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

Figure 3

In the first track or panel is the checkshot log,

the blue curve is the resulting P-wave curve after applying check-shot data on the original (blue) P-wave log

The checkshot corrected data for this well is questionable because the resulting curve is higher than the original curve

how can the checkshot corrected data be higher than the sonic data

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

Figure 3

In the first track or panel is the checkshot log,

the blue curve is the resulting P-wave curve after applying check-shot data on the original (blue) P-wave log

In this case the difference between the original curve and the result is small,

this means the drift between the sonic and checkshot was very small

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

b) De-spiking: Spikes in well log data can be caused by a number of factors,

for instance:  Ultra thin beds can cause constructive interference between the signals from the top and bottom boundaries of the thin bed (this is equivalent to what is known as “thinbed tuning” in seismic theory)

Fractured corridors: When an acoustic wave reaches a fluid filled fracture,

part of it reflects back into the rock and part changes to a fluid wave in the fracture

When the fluid wave reaches the opposite fracture wall,

there is further reflection loss and conversion back into compressional,

shear and Stonely waves (Henderson,

This can lead to spikes especially if the fractures are thinner (as is usually the case) than the log resolution

Cycle-skips: this occurs due to failure of the instrumental transponder to detect signal levels that are above the preset threshold at the instance of the first cycle

This may also cause abnormally low readings against an otherwise high background

Irrespective of their origin,

spikes are often undesirable in data because they represent data that is either erroneous or that is outside the log (and seismic) resolution

There are a number of ways to remove spikes from the well logs

Those that are available in e-logTM (the log editing module of Hampsom-Russell software) and also described by Handerson (2011) include the following: 1

Manually editing the section around the spiky section of the log

This method is excellent for removing obvious cycle-skips over short intervals but is tedious for long sections

Deleting sections of bad data and replace with realistic values or interpolate between the top and bottom of the deleted interval

This may facilitate creation of synthetics,

but valuable information may be lost

Using filters to remove questionable data

examples of such filters include: 3

However the method degrades the vertical resolution of the log

This method reduces curve variance in the filter window and eliminates unrealistic values

However,

valid data is "clipped" from peaks and troughs in thinly bedded formations

Isaac Bisaso,

Petroleum Geophysics

Chapter 3: Petrophysical modelling and analyisis

Replacing bad sections with rock physical estimates from other logs,

for instance a poor sonic section can be deleted and replaced by one synthesized from resistivity (see section 3

Blocking: Blocking a set of logs means replacing portions of them with one or more blocks,

simplifying the logs and allowing them to be easily edited

This process can be used to remove anomalous spikes (Hampson-Russell,

This is actually upscaling (to which we return in section 3

Results and discussion: In figures 3

6 and 3

The Check-shot data was first applied on each of the sonic logs before applying the “de-spiking” filter(s)

The velocity logs were very spiky especially in the reservoir zones,

this could be due to presence of thin cemented beds against a background of an otherwise clean sandstone reservoir zone,

but it could also be due to processing artifacts or instrumental problems and mis-measurements during acquisition

Efforts were made to reduce,

rather than eliminate the spikes,

first using a median filter and then a moving average filter

The aim was to compare and discern the capab

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