Interview with Ralf Oppermann: Using High-Resolution Fault Visualization from Seismic

Published
American Association of Petroleum Geologists (AAPG)

High resolution fault identification from seismic is an area that shows new promise, especially in finding compartments and new productive zones. High resolution fault volumes can be used to find small faults that have been intersected by wells and led to drilling problems (fluid losses, borehole stability issues, casing damage), as well as production problems. Fault volumes can be used to identify and e.g. shut off faults that are delivering water, or that allow cross-flow between zones and wells. The volumes can be also used to stay clear of faults, or target sweet spots or compartments in future wells.

Welcome to an interview with Ralf Opperman, who discusses the challenges and opportunities in identifying faults using seismic.

1. What is your name and your relation to the oil industry?

My name is Ralf Oppermann. I attained an MSc in Geology/Paleontology and a BSc in Business Economics from the University of Goettingen in Germany. During my studies, I worked for a number of oil companies as a vacation student in order to build up industry contacts. This eventually resulted in getting employed by Shell as one of their international staff. My first posting took me to Aberdeen in Scotland where I spent 1.5 years working offshore as a Petroleum Engineer, before joining an Asset team and working as a Production Geologist. My subsequent postings with Shell took me to Germany, Malaysia and New Zealand. During these years, I worked as a Seismic Interpreter and Geologist in integrated, multi-disciplinary Exploration, Appraisal and Development teams. After 15 years with Shell, I joined Chevron in Australia where I worked for 3 years as a Technical Advisor. In 2008, I set up a Consultancy company in Perth, Western Australia, to provide leading-edge resource characterization solutions to Oil & Gas and Mining companies.

2. How did you get involved in fault evaluations?

During my studies, my interest in faults was sparked when performing my mapping project in the folded and faulted Rhenish Slate Mountains, and doing my thesis research on a 150km long mineralised fault zone in Germany's Southeast (Bayerischer Pfahl, Bavarian Pillar). My Fluid Inclusion and Cathodoluminescence research on quartz samples from the Bavarian Pillar showed that this very large fault zone had been reactivated and healed multiple times which appeared to support the earthquake-driven seismic pumping model by Sibson et al. (1975).

During my first posting in the U.K., one of the more important things I learned was that the biggest and most underestimated risk for the development of many fields is compartmentalisation. If a field turns out to be compartmentalised by e.g. sealing faults, many more wells may be required to drain the same reserves, with a serious impact on project economics.

During my second posting with BEB, a former Shell-Exxon Joint Venture in Germany, I was asked to join a special Task Force that was set up to come up with new ideas on an old production province. I spent one full year working on fault seal issues in the Rotliegendes, trying to resolve issues that had never been fully understood previously. Supported by both Exxon and Shell Research Groups, we made some advances in developing a Fault Seal concept, utilising e.g. microstructural analysis of deformation bands in core to understand why faults were creating incredibly high pressure differentials between different compartments during production life. But we could not visualise or predict the location of faults forming these compartment boundaries from seismic data. Many years later, I understood that the reason why we couldn't see these faults was not related to the resolution of the seismic data at the time, but the lack of seismic volume interpretation technology that only came around years later.

3. What do you believe is the most significant development in terms of technology that you use in the last 10 years ?

With my Interpreter hat on, this is the development of advanced seismic processing and interpretation tools for me. When I started working in the industry 26 years ago, I was lucky enough to work with 3D seismic data rather than 2D data. But everyone at the time interpreted the data as if it was 2D seismic data, with a 2-dimensional, grid-based approach. Many volume-based interpretation workflows have been developed since, but uptake of these has been slow, despite the advantages of voxel-based interpretation and use of advanced attributes for the extraction of additional information from 3D surveys.

4. What have you worked on lately ? What are the main insights that this work has provided you?

I've spent a considerable amount of time systematically testing different attributes over the last 15 years, with the hope that there would be some that would allow me to reliably identify 3-dimensional geological features in seismic data. I focused on the identification of faults as I wanted to properly define and quantify compartmentalisation risk for the development projects I was working on, but also try and find a means to predict sweet spots in fractured and unconventional reservoirs. I delineated seismic discontinuities in seismic data using a variety of different attributes, and compared these to data that was derived independently of the seismic, such as fault and fracture evaluations from core and image log data. And finally came upon a few algorithms and processing settings that work exceptionally well in locating small-scale faults in seismic data. Since then, I've developed structural volume interpretation workflows that reliably visualize faults and fracture corridors at extremely high resolution, and that integrate these results with other seismic, well and also mine data to derive fully calibrated and deterministic fault network volumes.

The new fault volumes help to understand (and prevent) previous drilling and production issues in wells, such as fluid losses during drilling, geomechanical/borehole stability issues, well losses, casing damage through fault reactivation, reservoir cut-outs through faulting, water production, cross-flow between zones and wells, compartmentalisation & other issues. By delineating fluid barriers, fluid conduits or drilling hazards, the fault volumes can be used to optimize the drilling of wells, to find sweet spots or target compartments, to avoid water production and maximise the production of hydrocarbons.

The insights that have been gained through high-resolution Fault Extraction challenge a number of current Oil & Gas industry paradigms. I can only scratch the surface in this interview. Please contact me if you are interested in any of the below themes and would like more information and/or see Case Study examples.

  1. Automated fault extraction can support or replace manual fault mapping efforts, which are typically labour-intensive, time-consuming and imprecise. While manual fault interpretation creates an interpretation of a measurement, fault extraction creates a new measurement from an existing measurement. This measurement may prove to directly represent (deterministic) fault networks, or can be used as a starting point for subsequent manual interpretation and model generation. Automated fault extraction ultimately offers the opportunity to replace the subjective interpretation of faults with the objective measurement of faults and their properties directly from seismic data.
  2. Presently, most 3D surveys are under-utilized with respect to the detailed, high-resolution delineation of fault systems in the subsurface. Fault extraction can be applied to already existing 3D seismic data and help to identify the true fault resolution of a particular data set, not the perceived fault resolution that is typically established by visual (Interpreter) mapping only. With decreasing fault throw (i.e. reflector offsets) visual interpretation becomes more and more challenging and subjective, and visual fault mapping confidence decreases significantly, resulting in under-sampling of fault populations. This is where automated fault extraction allows a more objective, precise and complete delineation of fault populations in true 3-dimensional space, particularly 'sub-visual' faults with small displacement. This leads to an improved quality and achieves higher reliability compared to manual fault mapping and removes potential model-bias of an Interpreter. Automated fault extraction is based on the physical measurement of spatial variation in amplitude, phase and/or frequency content of 3D seismic data, and is as such free of bias and interpretation.
  3. 'Sub-visual' faults are currently incorrectly, but consistently and industry-wide, included into the sub-seismic and 'un-mappable' category by many Geoscientists, but can be extracted from seismic data with latest technology, experience and careful ground-truthing with other data. Automated Fault Extraction reduces the cut-off for fault recognition, both in terms of fault throw and also fault length, and can provide information on faults at sub-visual level, approaching the true seismic resolution limit for the detection of faults in a particular data set. As not all seismic attributes produce reliable and meaningful results, careful screening of a variety of different algorithm results and calibration with other data is required to find the best method for a particular objective level within a seismic data set. Specifically designed calibration workflows ensure that seismically derived fault networks are properly calibrated and ground-truthed with e.g. fault indications from other seismic, well, mine, drilling or production/flow data.
  4. The application of automated fault extraction techniques can help in narrowing or closing the scale gap between seismic and well data. Detailed integration work has e.g. revealed that perfect matches between seismically identified faults and faults identified from well data (image logs, cores, correlation, well tests, productivity, fluid losses etc.) can be found, thus allowing to close the scale gap between well and seismic data. In Oil & Gas, larger-scale faults are normally identified by 3D seismic interpretation, whereas small-scale faults are identified by spatially isolated 1D well data (primarily core and image logs). Faulting at medium scale (with displacements between ca. 30m and a few decimeter), however, is commonly neither recognized on seismic data nor well data (Gauthier & Lake 1993, Needham et al. 1996, Lohr et al. 2008). This typical scale gap between Oil & Gas well and seismic data is depicted in Figure 1. Large-scale faults that were visually interpreted from seismic and small-scale well-core fractures appear in this example to belong to the same continuous displacement population, which is described by a power law (Needham et al. 1996). The figure illustrates that medium-scale faults with throws between 30m and about 10cm are under-sampled or not sampled at all in both seismic and well data domains, creating a data gap. The cut-off (or left-hand truncation) for confident visual fault identification from seismic in this offshore U.K. field example is 20-30m, which is a typical cut-off for many deep reservoirs (e.g. Maerten et al. 2006). Automated fault extraction resolves the sub-visual fault domain and helps to shift the cut-off for fully sampled fault recognition to typically 5-8m of fault throw in deep seismic data sets (Figure 1). This shift helps to reduce the observed scale gap between seismic data and well data, and results in a multi-fold increase in the number of identified faults. It also results in an improved understanding of the effects that faults can have on fluid flow and hydrocarbon development activities, which are often masked by the scale gap. The Mining industry, in contrast to the Oil & Gas industry, is not affected by the scale gap between seismic and well data due to the generally higher density of data that is acquired in mining: high-resolution seismic, many wells and also mine tunnels that allow to sample faults over a wide range of scales. Shallow high-resolution seismic data acquired by the coal mining industry for example allows to visually map faults with throws as low as 1-3 m (e.g. Hearn & Hendrick 2001).

    Figure 1:

  5. Automated fault extraction increases fault resolution and results in a dramatic increase in the number of faults that are identified from seismic (Fig.1). Significantly higher fault/fracture densities are found than previously mappable or recognised. Instead of mapping e.g. 20 faults in a field, 200 or 2,000 faults can now be made visible, and their possible impact on drilling, mining and production activities can be investigated.
  6. With the increased fault resolution, stochastic modelling of fracture networks may not be required, as deterministic fault network data can be directly derived from seismic data and used to generate fully deterministic static fault models. When combined with fracture flow properties and geomechanical data, well-constrained and spatially exact flow simulations can be derived, that are of direct relevance for the understanding of historic well production data or the prediction of future well production.
  7. While it is recognized from outcrop studies that a few large faults are accompanied by many smaller-scale faults, it is most times unclear how these smaller-scale faults are spatially organised in the subsurface and how they may enhance or inhibit fluid flow. High-resolution fault network extractions performed for many different plays and in many different locations around the globe have consistently delineated larger tectonic faults and also smaller-scale fault networks that show 3-dimensional polygonal symmetries. These polygonal fault cells may represent diagenetic or compaction fractures which are formed during early burial and diagenesis and are likely related to diagenetically-induced shear failure (Cartwright 2011). Later reactivation of polygonal fault traces during tectonisation of rocks may lead to forced alignments of polygonal fault traces and the formation of larger tectonic faults.
  8. There are a lot more faults penetrated in wells than realised in the Oil & Gas industry, and these faults are often directly linked with a number of drilling and production problems, or production opportunities, in compartmentalised, tight, fractured and unconventional reservoirs, where faults in the subsurface can form fluid barriers or fluid conduits. Fault penetrations are often linked with drilling problems (e.g. gas kicks, fluid losses, borehole instability), as well as production issues (water production along fault planes, compartmentalisation) or production opportunities (access to productive natural fracture networks, 'sweet spots'). The new techniques can therefore provide a step-change in understanding drilling, production and safety issues in existing wells or mines.
  9. The new techniques can also be utilised to optimise future resource activities and recoveries, and increase the safety of future operations. Detailed fault imaging can reduce operational risks and costs, and can deliver exploration success as well as increased recoveries from resources. Faults linked to drilling, mining and/or production risks or hazards can be avoided. Safer, cheaper and more successful wells can be drilled by designing future wells (especially deviated/horizontal wells) to stay clear of faulted or fractured zones previously not predictable on seismic, or by predicting zones in the well where fluid losses, potential kicks and borehole instabilities could occur. Future hydrocarbon wells can be optimally placed with respect to fluid boundaries or fluid conduits, which is particularly important for the development of compartmentalised, tight, fractured, unconventional and structurally complex reservoirs. Fault intersections can be planned to drain different fault compartments (in matrix-producing fields), or to access the productive natural fault & fracture network.

For most resource plays, it is critical to improve the understanding, detection, modelling and prediction of fault and fracture networks. The key problem e.g. for the development of fractured resources is the difficulty to visualise the exact location and geometry of fractures (Lonergan et al. 2007).

This is where novel techniques and workflows in Automated Fault Extraction offer new opportunities to visualize fault networks at extremely high resolution. The workflows provide a completely new basis for the reliable identification and quantifiable prediction of fault networks in the subsurface, and allow to reliably delineate faults and predict fluid pathways in the subsurface.

A focused application of the new technology workflows can deliver increased recoveries from resources. And it can result in cheaper, safer and more successful drilling and mining operations. As such, the techniques are proposed as Best Practise tools for resource exploration and development planning and execution.

Figure 2 shows an example for the Austin Chalk/Eagle Ford Shale in Texas, where the input seismic looks very smooth, but well defined discontinuities can be identified through fault processing that represent small-scale faults. These small-scale faults with their attached fracture corridors have been proven to be present in wells, are widely distributed and laterally connected, and provide the main pathways for fluid flow in the Chalk and Eagle Ford Shale. The following video link provides some more information on the Eagle Ford Shale work that my company has performed: http://youtu.be/y2i2Ej7UPnk

If you have managed to read to the end of this interview, I would like to reward you by offering a free evaluation of your seismic data set and a few key wells that had drilling and/or production problems. I will show you the results of my work, and you can then decide for yourself whether this technology is improving your understanding of what is truly going on in the subsurface. I can promise you that the information we will extract from your seismic data will surprise you and help you resolve some of the apparent randomness in your drilling, exploration and production results. Or confirm what you already expected but couldn't prove from your data so far.

Anyone interested please or connect with me on LinkedIn - https://au.linkedin.com/in/opptimal

Thank you very much Susan for the opportunity for this interview!

Visit our website to learn about, “Making Money with Mature Fields” Geosciences Technology Workshop.

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