First Break - Volume 20, Issue 5, 2002

Despite the complexities of sound propagation in the earth, the model of the seismic reflection signal in the mind of the interpreter is a simple one. It is the convolutional model comprising a reflection coefficient series convolved with a time series representation of the seismic pulse in the zone of interest (Fig. 1). This pulse is often called the seismic wavelet. So, before starting to assign significance to the troughs and peaks of seismic data the interpreter needs to establish the form of the 'wavelet' in the data. This is not always as easy as it may seem. What is more, to counter this simple concept there is, unfortunately, a geophysical terminology that tends to confuse rather than simplify. More often than not terms are used loosely and inaccurately. A common question posed in discussions of seismic interpretations is 'what is the phase and polarity?' The question concerns the shape of the 'wavelet' and what, if any, is the sign (positive or negative) of the dominant part of the wavelet that relates to a particular contrast of acoustic impedance. As we shall see the question should be more specific and comprise a number of related questions: • Is there a dominant loop to the wavelet, and if so what is it? • Is there a time lag? • If the wavelet is not symmetrical why has the data not been zero phased? The motivation behind asking these questions is the need to know if and how the data can be used to reliably indicate hard and soft reflections, and what signature or response should be expected from different reflection types. Usually the enquirer will have in mind a relative amplitude model, for example that shale/brine sand reflections are hard and shale/gas sand reflections are soft. It may be slightly more sophisticated if there is also an AVO component to the model: for example if looking for shale/gas sand reflections that show increasing amplitude with offset (Fig. 2). In data processed for the purpose the form of the AVO response maybe diagnostic of lithology or fluid type. Understanding the wavelet shape is therefore a critical starting point in amplitude interpretation logic. The question of phase and polarity is simple enough. Invariably, however, it causes considerable debate and it is not unknown for these discussions to result in ill feeling and possibly loss of credibility for some poor interpreter. Before addressing the practice of estimating the interpreter’s wavelet the definitions of phase and polarity and their relevance to the seismic interpreter need to be addressed.

Few companies have experienced more of the possibilities and pitfalls of 'integration' than geoscience company Robertson Research International, which last year became part of the Netherlands-based Fugro Group. Andrew McBarnet reviews some of the company's eventful past and brings the story up to date with managing director Richard Fowler, a Scottisch geologist with a talent for corporate survival.

For more than 15 years, the E&P industry has been striving, not altogether successfully, to improve business decisions throug progressive integration of information, processes and technical domains. Based upon their experience Landmark Graphics specialists Hank Chambers, systems development director, integrated interpretation, and Dave Williams, systems development director, geophysical technologies, provide this frank assessment of the current state of integration in the upstream oil and gas industry and of what needs still needs to be done, viewing favourable recent advances in integrated visualization environments as the way ahead for improving E&P workflow efficiency.

Since the Department of Petroleum Engineering at Heriot-Watt University started its integrated programme at master's level in the broad area of reservoir description and simula- tion, 95 students have attended the MSc in reservoir evaluation and management and we hope to have our 100th graduate in September 2003. The experience has taught us a lot about cross-disciplinary training and the assessment of that training. The course was constructed to address a perceived niche in the market of courses on the understanding that: • Industry wanted people with a broad multidisciplinary training and formal training at master's level in geoscience and engineering • Industry professionals would seek to upgrade their skills in this area • A leading research institute in reservoir description and simulation (5* RAE 1996 and 2001) needed to be a mechanism for transferring things discovered back into the industry • Industry would eventually adopt the concept of a reservoir geoengineer as a natural evolution from reservoir geologist, reservoir geophysicist, petrophysicist or reservoir engineer The breadth of applications and the employment of our graduates over the years (Fig. 1) suggests that there is indeed a market for this sort of education.

Modern reservoir engineering needs a radical new approach as the present work-flow cannot, from a practical point of view, be inclusive of all the potential descriptions. The use of the 'Reservoir Geonome' concept is proposed as a new strategy to meet the following challenge. 'Geological models of reservoirs are growing in size by a factor of ten each year, whereas our ability to numerically simulate these is growing by a factor of two' - Killough (pers. com.). Nowadays, in order to capture the uncertainty in a field's development, it is not necessarily enough to simulate 100 realisations of one reservoir scenario. Asset development teams need hundreds of simulations for multiple scenarios in order to be able to asses the risk and opportunities for a single asset and to calculate the economic value added of an individual investment opportunity when added to a corporate portfolio. Studies have shown that performance outcome is related to geological complexity (Dromgoole & Speers 1997; Bos et al. 2001) and the benefit of reducing the parameter space to the minimum in order to make a business case. Regardless of the environment set by current oil prices, it is important to identify what are the really crucial parameters that need to be considered in field evaluations. These parameters might be drawn from the full range of the traditional upstream oil and gas disciplines: geophysics, geology, petrophysics, rock mechanics and engineering. A geoengineering approach (Corbett 1997) puts aside the traditional discipline groups in favour of concentrated, integrated effort in five cross-disciplinary sub-areas: architecture, properties, modelling, simulation, and management. In this cross-disciplinary team environment, it is possible to systematically explore various fundamental engineering problems, such as the effect of geological heterogeneity on flow performance; the effect of sandstone architecture on the seismic response; the relative contributions of stress and saturation changes to time-lapse seismic response through; and the well test response to a braided fluvial reservoir. To illustrate aspects of a geonomic approach we consider some of the simplified taxonomic classifications that can be used to map critical parameters in a systematic approach by considering specific cases within a global framework.

Sub-basalt seismic imaging is proving to be a major challenge for hydrocarbon exploration efferts along the NE Atlantic margin. Many of the prospective basins along this margin, from Norway to Ireland, are characterized by extensive ingenious activity of Early Tertiary age, which has resulted in the intrusive and/or extrusive emplacement within the sedimentary column of basaltic sills, dykes and lava flows (White & McKenzie 1989). These basaltic layers can vary in thickness and stratigraphic position.....

The Columbus basin contains some 20 000 ft of Plio-Pleistocene clastic sediments and a substantial number of oil and gas fields, which have been discovered by BG and other Operators. In the course of the various drilling campaigns, major overpressures have been encountered which pose a challenge to the safe and cost-effective drilling of wells (Heppard et al. 1998). In most cases the overpressure appears to be related to undercompaction of the rapidly buried shales, although other mechanisms, such as pressure transfer from deeper mobile shales, have also been identified. In the past, pore pressure prediction based on seismic data has been used in an attempt to improve upon the routine application of off-set well data. Such work was normally done in the 1D or 2D domain, and specifically targeted the local prediction of pressures at planned well locations. BG and Paradigm Geophysical have now extended this technology into the 3D domain by constructing pressure prediction cubes from 3D seismic data. Such pressure cubes allow a rapid scanning of alternative well locations, and also provide new insights in the 3D pressure distribution of the subsurface. This allows the recognition of sealing faults and may lead directly to the identification of previously unrecognized prospectivity. Figure 1 shows a sample seismic line from the 3D survey used for this project. The various sand packages (A-L) clearly stand out from the background shales.

A stratigraphic deconvolution has been carried out on the Fortuna National 3D seismic cube (Fig. 1) in collaboration with the Pemex Macuspana exploration business unit. The seismic inversion processing enabled a relationship to be established between the reservoir parameters, the acoustic impedance and the seismic response. In stratigraphic deconvolution processing, the 'classic' wiggle trace is replaced by a spiky response corresponding to a blocked acoustic impedance (AI) output. The spikes are coincident with those AI contrasts that are well aligned with meaningful geological boundaries. This is because the tuning effects of thin beds have effectively been removed in the inversion scheme. The procedure enhances the seismic resolution and discrimination potential in the zone under investigation. Seismic amplitudes should be preserved during the processing (see Hilterman 2001). The basic input for the model-driven inversion processing consists of: • a pre-stack time-migrated seismic cube (PSTM); • well logs of the MAC-201 borehole converted to time; • a 3D macro-layer model with continuous TWT horizons and initial acoustic impedance values. The current study was carried out in parallel with an AVO study. The AVO analysis required rigorous pre-processing of the seismic data. The seismic data cube, obtained in this way, was used in the final inversion procedure. The wavelet is extracted from the seismic cube by cross-correlation procedures. This is basically performed in a 1D manner and the result is therefore strongly influenced by local noise. The bulkshift for the logs is determined, and a zero-phasing operator for the seismic cube is computed. Based on the well-to-seismic match, the phase rotation is determined in order to obtain a zero-phase seismic cube. The 3D macro-layer model consists of TWT horizon surfaces with specification of the initial acoustic impedance values between them. The inversion algorithm creates microlayers in the model and perturbs the AI values. The perturbed AI trace is convolved with the seismic wavelet to compute a new synthetic trace. The difference between this synthetic trace and the original seismic trace is calculated at the same x-y position (Fig. 2). The program terminates the AI perturbation at a certain threshold value for this difference. It stores the AI model and goes on to the next trace. A true 3D approach is applied with several traces as input around the target trace. This approach stabilizes the outcome of the inversion, trace by trace. Testing is carried out to optimize the parameters of the inversion algorithm around the calibration well. The seismic inversion method never gives a unique result and several acoustic impedance models may equally explain the surface seismic response. By putting in constraints (e.g. geological, petrophysical), the number of solutions is reduced and the result is described by a most plausible scenario. The validity of this scenario is calibrated by the additional well control available and, in favourable circumstances, it will increase the interpreter's confidence in the correctness of the inversion procedure. The results of the inversion are summarized in AI section plots and layer maps (microlayers). Meaningful reservoir characteristics are to be extracted with care from the inverted acoustic impedance cube. In this respect, a multidisciplinary approach with close cooperation between the seismic processor, the interpreter, geophysicist, geologist, reservoir engineer and petrophysicist can be considered of prime importance.