Quantitative Seismic Interpretation Research Articles

Vibroseis source improvements towards broadband land acquisition

Zhouhong Wei and Yuanzhou Pan demonstrate how to improve the amplitude through hydraulic and mechanical improvements and explore whether vibrator control electronics effectively supress harmonic distortion at low frequencies. Increased seismic frequency bandwidth towards both low and high frequencies can improve image resolution, reservoir characterization and quantitative seismic interpretation. However, using the vibroseis method to acquire broadband seismic data is very challenging in land data acqusition. At low frequencies, due to mechnical and hydraulic limitations, such as limited reaction mass stroke, the pump flow and harmonics, it is very difficult for a seismic vibrator to produce sufficient ground force at fundamental frequencies. At high frequencies, due to low rigidity of the vibrator baseplate resulting in poor coupling, it is also difficult for the vibrator to efficiently radiate the ground force into deep ground. Often, the high-frequency force energy produced by the hydraulic system is wasted in bending the baseplate. Therefore, the first challenge in broadband vibroseis data acqusition is to improve the vibrator performance so that the vibrator can produce sufficient force with a good S/N ratio in a wide frequency bandwidth.

Thin beds, tuning, and AVO

In this tutorial, we will explore two topics that are particularly relevant to quantitative seismic interpretation — thin-bed tuning and AVO analysis. Specifically, we will examine the impact of thin beds on prestack seismic amplitudes and subsequent effects on AVO attribute values.

AVO-derived attributes to differentiate reservoir facies from non-reservoirs facies and fluid discrimination in Penobscot area, Nova Scotia

The discrimination of hydrocarbon (gas or oil) facies from non-hydrocarbon (wet sands) facies is an important goal in quantitative seismic interpretation and reservoir estimation. The differentiation of hydrocarbon facies from non-hydrocarbon in Mississauga Formation of early Cretaceous in Penobscot area is a difficult task due to smaller net pay thickness and shaly sand intervals. Based on seismic interpretation and wireline logs, five sand plays are identified in the middle of Mississauga Formation. Four sands have hydrocarbons while top of sand 5 represents hydrocarbon water transition zone. Among these four, the pay sand 4 is analyzed for hydrocarbon facies. Wireline logs and seismic data are used to derive various amplitude versus offset (AVO) based attributes such as: acoustic (I P ) and shear (I S ) impedances, Poisson ratio (σ) etc. Further, the combined attributes e.g., product of Lame parameters (μ, λ) with density (ρ), their ratio (λ/μ), difference between bulk modulus (K) and shear modulus (μ), Δμρ/μρ, Δ(λ/μ + 2)(λ/μ + 2) and the pore space modulus (K P ) are also analyzed to find out the best attribute as a hydrocarbon facies discriminator from non-hydrocarbon facies in the shale imbedded pay sand 4 of Mississauga Formation. First, petrophysical parameters such as P and S wave impedances, Lame’s parameters etc. are extracted from log data. Then, appropriate pairs of seismic attributes are crossplotted so that the hydrocarbon and non-hydrocarbon facies cluster together for quick identification and interpretation. Gamma ray index is crossplotted against spontaneous potential log to mark sand and shale facies. Fluid substitution modeling for various fluid types and saturation is also done which demonstrate that the cross-plots between different rock physics parameters can be used to distinguish between reservoir fluids. Our analysis reveals single P-wave based attributes are not sufficient to discriminate fluids thus the use of multi-attributes such as K P , λρ and K-μ is more effective to discriminate the hydrocarbon and non-hydrocarbon facies. The analysis of these cross-plots was done to map the reservoir sands and the hydrocarbon-water contact.

Probabilistic approach to rock physics modeling

Rock physics modeling aims to provide a link between rock properties, such as porosity, lithology, and fluid saturation, and elastic attributes, such as velocities or impedances. These models are then used in quantitative seismic interpretation and reservoir characterization. However, most of the geophysical measurements are uncertain; therefore, rock physics equations must be combined with mathematical tools to account for the uncertainty in the data. We combined probability theory with rock physics modeling to make predictions of elastic properties using probability distributions rather than definite values. The method provided analytical solutions of rock physics models in which the input is a random variable whose exact value is unknown but whose probability distribution is known. The probability distribution derived with this approach can be used to quantify the uncertainty in rock physics model predictions and in rock property estimation from seismic attributes. Examples of fluid substitution and rock physics modeling were studied to illustrate the application of the method.

Tuning is not necessarily bad—Channel sand delineation from seismic data of the Upper Gharif in Oman

Upper Gharif reservoirs contribute to a significant portion of Petroleum Development Oman (PDO) reserve and resource volumes. To drill and encounter channel sands in this formation have been historically challenging. Previous attempts to predict the location and extent of the sands were discouraging. As such, grid drilling is employed in several fields, which is costly, especially in deep reservoirs. However, a breakthrough was made in Field Z. The three existing wells show different seismic characteristics. Suspecting that focus on high-tech issues such as seismic inversion might have been a distraction instead of adding value and that thickness below tuning might not always be a bad thing, a 2D model with two sand wedges was made. The model showed that synthetic seismic amplitude varies when sand thickness varies and that seismic trace shape changes too. These observations supported a decision to drill an amplitude hot spot, where good sand presence was predicted. Results from this and three additional wells have all confirmed sand/shale lithology predictions from seismic data. Wells, synthetic data, and real seismic data converge on one conclusion: The absence of thick sands eliminates the well-known tuning-related nonunique correlation between seismic amplitudes and sand thickness. In other words, tuning becomes a good thing. Neither an innovative concept nor cutting-edge technology was used. The work might seem primitive to people who are used to good-quality seismic data where thin beds are illuminated via frequency decomposition and RGB blending in a routine workflow. However, it is the first successful case of the Gharif Formation in Oman, where previous seismic studies resulted in an overall disbelief in using seismic data for reservoir characterization. This work proved that reservoir distribution in Field Z is predictable and thus there is no need for expensive grid drilling. Going back to geophysical basics and not overlooking thin layers, this success has not only played a key role in the field-development plan study of Field Z but also has rejuvenated PDO's interest in quantitative seismic interpretation of the Upper Gharif.

Differentiating between potential reservoirs and hardrock with a holistic quantitative seismic interpretation method

SUMMARY Easily recognized bright events on seismic data can infer a host of phenomena, ranging from lithology interplay to indicating lucrative prospects in relatively greenfield settings. The availability of seismic angle stacks integrated to a geological model afford the petroleum explorationist more options to reduce obviously incorrect conclusions and help to identify the more likely cause(s) of these bright events within the seismic data. A combination of simultaneous seismic inversion, and far and near angle stack comparisons gave more confident deductions on certain recognized seismic features as well as identifying potential interesting prospects to pursue and study in the context of petroleum exploration in offshore Myanmar. In the Bay of Bengal Rakhine Outer Fold Belt, exploration 3D seismic acquisition was carried out in 2010 and a significant gas discovery made in 2012. We present some examples of features that were identified on seismic data and how the range of geological hypotheses could be narrowed by seismic inversion.

Rock Physics workflow including application of the Xu-Payne multi-porosity model on chalk reservoirs on the western platform, UK North Sea

D.M. Hammond and S.S. Payne present an application of the new Xu-Payne model, which classifies carbonate rocks as dual porosity systems, to investigate the fluid effects within the chalk play of the Central Graben for quantitative seismic interpretation. The model was calibrated using log data from three wells on the western platform of the UK North Sea.

Broadband Seismic: Ultimate Input for Quantitative Seismic Interpretation

This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 150821, ’Broadband Seismic: The Ultimate Input for Quantitative Seismic Interpretation,’ by Cyrille Reiser, Euan Anderson, and Yermek Balabekov, Petroleum Geo-Services, prepared for the 2012 SPE North Africa Technical Conference and Exhibition, Cairo, 20-22 February. The paper has not been peer reviewed. Predicting reservoir properties away from the well using seismic information is key to quantitative interpretation. Quantitative seismic interpretation combines well and seismic data with seismic interpretation or early geological information. This proposed workflow is integrated, and the quality and accuracy of individual constituents are highly important to estimate the volume of hydrocarbon in place in a particular reservoir interval accurately. If seismic data contain very strong low-frequency information and the seismic image is of high quality/resolution, it is possible to estimate the absolute impedance directly at each point within a seismic volume. Introduction The ultimate goal in finding the best places to drill exploration or production wells is to characterize the physical properties of rock formations before drilling. Ideally, it would be desirable to have quantitative information regarding key rock properties (e.g., lithology, porosity, clay content, and net-/gross-pay ratio) along with information regarding fluid types, saturations, and pore pressures of potential reservoirs. Historically, seismic images have not delivered this information because the seismic bandwidth was limited by the conventional- streamer design and acquisition method. The conventional-streamer acquisition with a single type of sensor (hydrophone) and conventional towing depth fails, in most cases, to fulfill the geoscientist requirement. Therefore, broader bandwidth is needed with as much low- and high- frequency extension as possible. There is an urgent need to characterize very complex reservoirs accurately and be able to resolve very thin remaining hydrocarbon columns. To achieve an improved seismic characterization of the reservoir and better reservoir- property prediction away from the well, high-quality broadband seismic is needed. Well data offer high vertical resolution but do not provide lateral information, whereas seismic data have densely spaced traces with limited vertical resolution. Quantitative interpretation, or seismic reservoir characterization, is a work-flow that combines well and seismic data with seismic interpretation or early geological information. This workflow is highly integrated, and the quality and accuracy of individual constituents are of great importance to develop an accurate estimate of the hydrocarbon-in-place volume in a particular reservoir interval with the main aim being to optimize the drilling location on the basis of 3D seismics. Data Acquisition New acquisition methods and technologies have been introduced to provide a broader seismic bandwidth. Advances include variable inline-streamer towing depth, acquisition with streamers at different constant depths, and dual-sensor towed streamer. The dual-sensor (combination hydrophone and vertical-velocity sensor) towed streamer enables effective removal of the sea-surface ghost by use of wavefield separation, allowing capture of the full bandwidth of the upcoming wavefield.

Enhanced dynamic interpretation from correlating well activity to frequently acquired 4D seismic signatures

Quantitative 4D seismic interpretation can be successfully achieved by exploiting the causal link between the temporal variation in well activity and the 4D seismic signatures they induce. This is achieved by capturing in mathematical form the common interpretational practice of identifying the origin of dynamic signals in the 4D seismic volumes or maps on the basis of their association with a particular injector or producer. Thus, for example, a region of reservoir hardening (impedance increase) around a producer may be interpreted as a signal of pressure decrease. Similarly, an area of softening (impedance decrease) around an injector is interpreted as a signal of pressure increase when pressures are above bubble point. In the literature, a hardwired integration between the seismic and engineering domain has been obtained to some extent using methods such as seismic history matching, where the observed seismic and well production history data are simultaneously fit by predictions from a common simulation model. However, this approach is computationally expensive and suffers from nonuniquenesses, inaccuracies in the petroelastic model, and is ultimately only as accurate as the model itself. As an alternate approach, we reconcile here the well production history and time-lapse seismic data in the data domain without the need for a model. This approach involves the use of many frequently repeated seismic surveys shot over the same field, and mathematically correlates changes in the mapped seismic attributes directly to the fluid volumes injected and produced from the wells. Thus, well data normally used exclusively for history matching in the reservoir engineering domain can now also be directly integrated with the time-lapse seismic data.

R&D on Campus: Evolving Operations Drive New Technologies

Editor’s note: JPT surveyed major universities around the world regarding their key upstream research and development projects and activities. The second installment of this series will be published in the July issue. The signposts ahead show that applied, improved technologies such as enhanced oil recovery (EOR), nanotechnology and state-of-the-art reservoir treatment, monitoring and modeling, and unconventional extraction will define the future of the oil and gas industry. As the demand and need for these techniques and technologies increase, research is under way at numerous universities on how to improve recovery factors to meet future world consumption. Institute of Petroleum Engineering, Heriot-Watt University, UK Petroleum engineering at Heriot-Watt University comprises eight general areas of expertise: geomechanics and rock physics, hydrates, hydrocarbon recovery mechanisms, production chemistry, reservoir description, reservoir fluids, reservoir geophysics, and uncertainty quantification. Patrick Corbett, Total professor of petroleum geoengineering, said the reservoir description group is a coming together of joint industry projects at the university. Furthermore, this kind of collaborative, interdisciplinary model is evolving into research networks among multiple universities working on the same project, he said. Industry collaborators with the institute include Shell, ExxonMobil, BG Group, Weatherford, Wintershall, PetroChina, Schlumberger, BP, Chevron, Conoco Phillips, Hess, Landmark, Maersk Oil, Marathon Oil, Petrobras, Statoil, Total, Anadarko, Saudi Aramco, and Eni. The reservoir description group specializes in the integration of geology and engineering for improved quantitative reservoir performance characterization. The reservoir geophysics group’s seismic history matching project uses Schlumberger’s Eclipse, Frontsim and Petrel reservoir data software, Roxar RMS process management software, and Malcolm Sambridge’s NA Algorithm, approaching the subject with geological modeling. The group’s Edinburgh Time-Lapse Project develops reservoir analysis tools for quantitative seismic data interpretation. The main consortium of the production chemistry group is a flow assurance and scale team joint industry project that develops software used to design field scale inhibitor treatments. The reservoir fluids group collaborates with carbon dioxide injection research in partnership with the European Carbon Dioxide Thematic Network, the European Network of Excellence on Geological Storage of Carbon Dioxide, the UK Carbon Capture and Storage Edinburgh Consortium, and the Scottish Centre for Carbon Storage. The uncertainty quantification group is in its third phase, addressing problems of uncertainty and error modeling in production. The geomechanics and rock physics group developed the true triaxial Smart Cell for measurement of stress-sensitive petrophysical and rock property data, and created the spinout company PetroMagnetics.

Shell's integrated quantitative interpretation workflow

Shell Development Australia is a major asset holder in the Browse Basin and the Carnarvon Basin in the North West Shelf of Australia. In 2007, Shell Development Australia embarked on an integrated quantitative seismic interpretation project related to the Triassic Mungaroo Formation in the Carnarvon Basin. The main objective was to constrain the uncertainties in using seismic data as a predictor for rock and fluid properties of fields and prospects in the basin. This project followed a workflow that has been proven in other basins around the world, whereby the vertical and lateral variability of rock properties of both reservoir and non-reservoir lithologies are captured in general trends. The calculated trends are based on well log extractions of end member lithologies and the input of petrographic information and forward modelling. In combination with a regionally consistent 3D burial model for the estimation of remaining porosity, these established rock trends then allow for a prediction of various acoustic responses of reservoir and pore fill properties. The comparisons between the pre-drill predicted rock properties and the properties encountered after drilling at different reservoir levels have lead to a general confidence that the reservoir properties can be derived from seismic data where well data are not abundant. This increased confidence will play a major part in Shell’s attitude towards appraisal activities and decisions on various development options.

Quantitative Seismic Interpretation for Characterizing Carbonate Diagenesis – an Elk/Antelope Gas Field Study.

Quantitative Seismic Interpretation for Characterizing Carbonate Diagenesis – an Elk/Antelope Gas Field Study.

Rock-physics diagnostics of depositional texture, diagenetic alterations, and reservoir heterogeneity in high-porosity siliciclastic sediments and rocks — A review of selected models and suggested work flows

Rock physics has evolved to become a key tool of reservoir geophysics and an integral part of quantitative seismic interpretation. Rock-physics models adapted to site-specific deposition and compaction help extrapolate rock properties away from existing wells and, by so doing, facilitate early exploration and appraisal. Many rock-physics models are available, each having benefits and limitations. During early exploration or in frontier areas, direct use of empirical site-specific models may not help because such models have been created for areas with possibly different geologic settings. At the same time, more advanced physics-based models can be too uncertain because of poor constraints on the input parameters without well or laboratory data to adjust these parameters. A hybrid modeling approach has been applied to siliciclastic unconsolidated to moderately consolidated sediments. Specifically in sandstones, a physical-contact theory (such as the Hertz-Mindlin model) combined with theoretical elastic bounds (such as the Hashin-Shtrikman bounds) mimics the elastic signatures of porosity reduction associated with depositional sorting and diagenesis, including mechanical and chemical compaction. For soft shales, the seismic properties are quantified as a function of pore shape and occurrence of cracklike porosity with low aspect ratios. A work flow for upscaling interbedded sands and shales using Backus averaging follows the hybrid modeling of individual homogenous sand and shale layers. Different models can be included in site-specific rock-physics templates and used for quantitative interpretation of lithology, porosity, and pore fluids from well-log and seismic data.

Wavelet extraction assessment for quantitative seismic interpretation

Wavelet extraction assessment for quantitative seismic interpretation

Study of petrophysical parameter sensitivity from well log data

Proper analysis of petrophysical parameter sensitivity from well log data can greatly improve the ability to discriminate hydrocarbon-bearing rocks. In this paper we discuss a petrophysical analysis method for the selection and application of higher sensitivity seismic attribute parameters to improve the ability to discriminate fluid and lithology. To better integrate with seismic interpretation, we construct a template to highlight rock physics parameters in sensitivity space, providing guidance for the quantitative seismic interpretation of hydrocarbon-bearing reservoirs.

Abstract: The First-Order and the Second-Order Seismic Textures: Implications for Quantitative Seismic Interpretation and Hydrocarbon Exploration 

Abstract: The First-Order and the Second-Order Seismic Textures: Implications for Quantitative Seismic Interpretation and Hydrocarbon Exploration