Quantitative Interpretation Workflow Research Articles

A Pareto Multi‐Objective Optimization Approach for Anisotropic Shale Models

AbstractWhen the elastic parameters of rocks such as shales vary with the propagation direction of a passing seismic wave that rock is called anisotropic. Modern seismic data are typically acquired over a wide azimuth range using long offsets. For such data, optimum imaging and subsequent quantitative amplitude interpretation requires taking subsurface anisotropy adequately into account during execution of the quantitative interpretation workflow. Here, we present a multi‐objective approach to find an optimal solution using both well logs and seismic data. We include minimization of a seismic match objective function as a constraint to narrow the uncertainty distribution around model parameters and to ensure that the model is consistent with seismic as well as well log data. Consequently, the resulting anisotropy model can be used for both well logs and seismic data analysis. First, the Hudson‐Cheng crack model is used to obtain rock matrices properties and aspect ratio of a set of ellipsoidal cracks. Then, we obtain a set of non‐dominated solutions, which minimizes the rock physics model multi‐parameter objective function and the Amplitude‐Versus‐Offset objective function. The approach is applied to field data with one vertical well log and pre‐stack migrated seismic data. In spite of the low signal‐to‐noise ratio of the seismic data, the overall results are consistent with the rock physics model and fit the seismic amplitude variations, particularly for the confidently interpretable mid‐to‐far angles of incidence. Furthermore, we extend the approach to predict seismic anisotropy away from well calibration, to map the occurrence of high‐quality organic‐rich shales.

Testing popular rock-physics models

In the spirit of classic rock physics, and as an ideal foundation for conventional quantitative interpretation workflows, we consider several popular models relating elastic rock properties to their composition, microstructure, and effective stress on the background of a worldwide log data set, incorporating sands and shales characterized by the maximum dynamic impedance range. We demonstrate that the patchy cement model, ellipsoidal inclusion model, and siliciclastic diagenesis model may be calibrated successfully against the world data set and used in seismic rock property log restoration/editing. We also demonstrate that some of these models present obvious challenges in terms of the information derived from quantitative seismic interpretation. Notably, the key input parameters used in these rock-physics models may show little resemblance to the rock parameters actually observed in geologic studies. Replacing the true rock parameters with the effective ones may do disservice to the science of rock physics in general.

Quantitative Interpretation: Use of Seismic Inversion Data to Directly Estimate Hydrocarbon Reserves and Resources

A quantitative interpretation workflow utilising AVO inversion based lithology prediction data was developed to directly assess reserves and resources for an LNG development project in the Carnarvon Basin. The study area is covered by modern MAZ PSDM 3D seismic data using broadband acquisition and processing techniques, calibrated by numerous well intersections of the Triassic Mungaroo Formation reservoirs. Interpretation of the fluvio-deltaic reservoir bodies can be somewhat interpretive using ‘traditional’ workflows. By interpreting chronostratigraphic events tied to well-based biostratigraphy and then using the lithology prediction volumes, the interpretation of reservoir bodies becomes more objective. Seismic inversion data are typically used to qualitatively guide resource assessments, through amplitude mapping or use in static and dynamic modelling. In this case study, the inversion based prediction volumes are used to extract P90, P50 and P10 sand geobodies which are directly input into probabilistic reserve and resource assessments. The workflow is applied to discovered, developed, undeveloped and prospective reservoirs. Geobody extraction required the PSDM depth data to be accurately calibrated to wells. A calibrated velocity model was built by perturbing the imaging velocities in a 3D model to tie the chronostratigraphic events associated with all the reservoir intervals. Fluid contacts derived from wells were used to provide a depth cut-off to the geobody extractions. The resulting reserve and resource assessments from this workflow show an excellent match with previous assessments including static and dynamic modelling methods. The geobodies also identified previously unrecognised channel sands not easily interpreted on full and angle stack data.

Integrated shale-gas reservoir characterization: A case study incorporating multicomponent seismic data

The objective of this case study is to predict geologic properties of a shale reservoir interval to guide production and completion planning for successful development of the reservoir. The conditioning, analysis, and blending of the converted-wave (PS) seismic data into a quantitative interpretation (QI) workflow are described in detail, illustrating the successful integration of geologic information and multiple seismic attributes. A multicomponent 3D seismic survey, several wells with dipole sonic logs, and a multicomponent (3C) 3D vertical seismic profile are available for the study. For comparisons of the incremental value of PS data, the QI workflow is completed entirely using only PP data and then modified and redone to incorporate information from the PS data. Predictions of the geologic properties for both workflows are assessed for accuracy against the existing well log and core evidence. Determining reservoir properties of the shale units of interest is important to the successful placement of horizontal wells for efficient multistage hydraulic fracturing and maximum gas production. Although conventional interpretation of conventional seismic data can only provide reservoir geometry, the quantitative analysis of prestack multicomponent data in this study reveals detailed distinctions between reservoir units and relative measures of porosity and brittleness bulk properties within each unit. Using all of the elastic properties derived from the seismic data analysis allowed for the classification of lithological units, which were, in turn, subclassified based on unit-specific reservoir properties. The upper reservoir units (Muskwa and Otter Park) were shown to have more variability in brittleness than the lower reservoir unit (Evie). Validation at a reliable well control confirmed these distinctive units and properties to be very high resolution and accurate, particularly when the PS data were incorporated into the workflow. The results of this method of analysis provided significantly more useful information for appraisal and development decisions than conventional seismic data interpretation alone.

Bringing multidisciplinary geosciences into quantitative inversion: A Midland Basin case study

Over the last two decades, the driver behind the boom in unconventional reservoirs has been the development of horizontal drilling and hydraulic fracturing. More recently, the fall in oil prices has resulted in the industry refocusing on the shale market and its most profitable plays, a notable example being the Permian Basin. We argue that this refocusing should also take place in the technology domain and that seismic and its derivatives should provide reservoir and completion engineers with the means to optimize well planning. This is illustrated with a case study of an advanced quantitative interpretation workflow tailored for a seismic multiclient program in the Wolfberry Play of the Midland Basin. Seismic imaging begins by providing a structural interpretation basis, then quantitative interpretation provides 3D elastic and geomechanical attributes through prestack inversion and azimuthal inversion, respectively. This 3D canvas of elastic attributes is combined with petrophysical, mineralogical, geomechanical, and geochemical properties measured at the wells. The challenge of reconciling such data sets with different scales and spatial sampling is overcome using physical, empirical, or statistical relationships within the data. The adjunction of rock data to the 3D elastic attributes provides calibration and validation of the inversion results. More interestingly, it allows for quantitative prediction of lithology, facies, porosity, and geochemical properties away from the wells. The purpose of the resulting calibrated reservoir model is to assist in optimizing drilling plans and executing completion designs.

Utilizing a novel quantitative interpretation workflow to derisk shallow hydrocarbon prospects — a Barents Sea case study

Large areas of the Barents Sea, such as the formerly disputed zone between Norway and Russia in the eastern Barents, are still undrilled. An exploration licence was recently awarded over the Haapet Dome (PL859) to operators with a prime interest in shallow Jurassic reservoirs (Reiser et al., 2016). Multiple oil and gas discoveries made farther west, such as Goliat and Norvarg, make this part of the world a highly prospective area for hydrocarbons. Traditionally the combination of a hard seabed with relatively shallow water depths has prevented the recording of near offset reflection data for the shallowest sediments. It is therefore not surprising that the presence of shallow hydrocarbons in this area has only recently been revealed thanks to imaging techniques that use the energy from sea-surface reflections which provide the missing near angle information for reliable AVA analysis. This paper describes a regional rock physics study of the Barents Sea and a quantitative interpretation workflow using Separated Wavefield Imaging (SWIM) and Full Waveform Inversion (FWI) to identify leads over the Haapet Dome in absence of direct well information.

CSEM anomaly identification

In a sedimentary basin, everything typically present is highly resistive, except brine. A localized region of higher resistivity, whether identified on well logs or from controlled-source electromagnetic (CSEM) data, is therefore indicative of a local reduction in interconnected brine content. This may be due to the presence of fresh water, low-porosity lithologies (including salt, volcanics and some types of carbonate), or hydrocarbons. It is this first-order sensitivity to fluid presence and properties that makes CSEM information of high potential value in an exploration environment (Baltar et al., 2015; Fanavoll et al., 2014; Zweidler et al., 2015). In its simplest form, the use of CSEM for hydrocarbon detection can be considered a two-stage process. First, localized regions of higher resistivity need to be identified from the CSEM data. Second, these ‘anomalous’ regions must be interpreted in terms of their potential for being indicative of hydrocarbon presence. One might reasonably expect that the greater challenge is the latter: successfully predicting the geological cause of an anomalous resistivity. However, in our experience, the initial task of reliably identifying the anomalous features can prove equally challenging without an appropriate process. Early CSEM interpretation workflows, focusing on measurement interpretation, tended to use a ‘threshold normalized amplitude response’ (NAR) rule such as 15% (Hesthammer, 2010). This proved useful in the most simple geologies, but of less value in more complex settings, and also failed to account for relative data quality. Today, the starting-point for CSEM interpretation is subsurface resistivity images. With these, there still exists plenty of leeway for the choice of colour scale to have a large effect on the apparent sizes of any ‘red blobs’ in the study area. We detail here a simple and clear criterion to qualify a feature as anomalous with respect to its surroundings, which is analogous to that followed when qualifying the significance of seismic amplitude anomalies (Roden et al., 2014). We expand on an approach first proposed in Baltar and Roth, 2013, as part of a quantitative interpretation workflow for CSEM, providing a more practical guide to its application and implications. The concept is first illustrated with well data, before the method is detailed with CSEM examples.

North Sea case study: Heavy oil reservoir characterization from integrated analysis of Towed Streamer EM and dual-sensor seismic data

Integrated analysis of geophysical data can provide valuable information on reservoir properties, on the basis of which exploration, appraisal, and development decisions can be made. Hence, we have introduced a quantitative interpretation workflow that integrates dual-sensor seismic and Towed Streamer controlled-source electromagnetic (CSEM) data. The workflow was designed to facilitate a reliable extraction of the complementary information from the two datasets. The seismic contribution starts with a depth-converted sparse horizon model to initialize the EM inversion, but it is not placed rigidly. This makes good sense when taking into account the uncertainties in seismic data, in the time to depth conversion, and more importantly, the fact that a reservoir can be hydrocarbon-charged to an unknown degree corresponding to the spill-point or less. We show how this approach enables a robust and reliable workflow for integrating EM and 3D seismic data with data examples acquired in an area with the complex geology of the Bressay, Bentley and Kraken (BBK) fields in the North Sea. The three heavy oil reservoirs are injectites, located in close proximity to other high resistivity settings, such as the shallow gas in the overburden, regional Balder Tuff and granite intrusions, resulting in challenging imaging issues.

Effect of micro‐inhomogeneity on the effective stress coefficients and undrained bulk modulus of a poroelastic medium: a double spherical shell model

ABSTRACTAlthough most rocks are complex multi‐mineralic aggregates, quantitative interpretation workflows usually ignore this complexity and employ Gassmann equation and effective stress laws that assume a micro‐homogeneous (mono‐mineralic) rock. Even though the Gassmann theory and effective stress concepts have been generalized to micro‐inhomogeneous rocks, they are seldom if at all used in practice because they require a greater number of parameters, which are difficult to measure or infer from data. Furthermore, the magnitude of the effect of micro‐heterogeneity on fluid substitution and on effective stress coefficients is poorly understood. In particular, it is an open question whether deviations of the experimentally measurements of the effective stress coefficients for drained and undrained elastic moduli from theoretical predictions can be explained by the effect of micro‐heterogeneity. In an attempt to bridge this gap, we consider an idealized model of a micro‐inhomogeneous medium: a Hashin assemblage of double spherical shells. Each shell consists of a spherical pore surrounded by two concentric spherical layers of two different isotropic minerals. By analyzing the exact solution of this problem, we show that the results are exactly consistent with the equations of Brown and Korringa (which represent an extension of Gassmann's equation to micro‐inhomogeneous media). We also show that the effective stress coefficients for bulk volume α, for porosity nϕ and for drained and undrained moduli are quite sensitive to the degree of heterogeneity (contrast between the moduli of the two mineral components). For instance, while for micro‐homogeneous rocks the theory gives nϕ = 1, for strongly micro‐inhomogenous rocks, nϕ may span a range of values from –∞ to ∞ (depending on the contrast between moduli of inner and outer shells). Furthermore, the effective stress coefficient for pore volume (Biot–Willis coefficient) α can be smaller than the porosity ϕ. Further studies are required to understand the applicability of the results to realistic rock geometries.

Incorporating 3C seismic data quantitatively for enhanced geologic detail in an oil sands reservoir

A workflow incorporating converted-wave (PS) data in an integrated quantitative interpretation (QI) was illustrated with a case study in the Canadian oil sands in which multiple data types were available including high-quality 3D multicomponent data, dipole sonic logs, and a multicomponent walk-away vertical seismic profile (VSP). In an area with unconventional rock property behavior and complex fluid distributions, the dipole sonic logs provided the data necessary for robust deterministic rock-physics templates. The VSP was essential in depth-registering the P-wave surface seismic with the PS-wave data and in determining the appropriate phase rotation of each data set. The 3D multicomponent seismic was used to derive a variety of separate and joint attributes incorporating amplitude variation with offset, prestack and poststack inversion and multiattribute processes. Finally, all elements of the workflow were combined in an interactive classification procedure for optimum representation of geology in the seismic volume. Results of the QI workflow with and without the PS data were compared with each other, and ultimately, to blind wells to assess the potential benefits of including PS data. The comparison showed that better prediction of fluid properties, without compromising P-wave data resolution, was possible when PS data were included.

Value of broadband seismic for interpretation, reservoir characterization and quantitative interpretation workflows

Cyrille Reiser, Tim Bird, Folke Engelmark, Euan Anderson and Yermek Balabekov present a series of case studies to illustrate the performance of broadband seismic dual-sensor streamer acquisition and its positive impact across a range of E&P asset development phases from exploration to appraisal and field development/optimization.

Field appraisal and accurate resource estimation from 3D quantitative interpretation of seismic and CSEM data

The key questions in field appraisal are: What is the hydrocarbon volume, and how are the hydrocarbons distributed in the field? The ability to answer these questions accurately is critical for deciding whether to produce a field and for developing a production plan. Wells drilled during the appraisal phase provide well and flow-test data, which are combined with structural knowledge from seismic surveys to map the extent of the field and generate a reservoir model. The cost for appraising an offshore field can exceed US $100 million, and it is desirable to obtain the information required with fewer wells if possible. Quantitative interpretation of surface geophysical data provides reservoir properties between well locations and can, therefore, significantly reduce appraisal costs. A quantitative analysis of seismic data using well-log information will typically determine reservoir rock porosity. Other important parameters are hydrocarbon saturation, permeability, and net-to-gross ratio. Quantitative interpretation of several reservoir properties using only the seismic data is associated with significant ambiguity. To determine several of these parameters accurately, complementary geophysical data sets with different sensitivity characteristics are needed. Controlled-source electromagnetic (CSEM) data are sensitive to the fluid type and can provide additional information to determine the hydrocarbon saturation more accurately. We have developed a new quantitative interpretation workflow integrating seismic and marine 3D CSEM data for estimating the hydrocarbon volume and obtaining 3D distributions of the hydrocarbon pore volume. A performance test of the workflow has been carried out on the Troll West Oil Province (TWOP) in the Norwegian North Sea (Figures 1 and 2). This article describes our methodology and presents encouraging results.

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.

Drilling success as a result of probabilistic lithology and fluid prediction—a case study in the Carnarvon Basin, WA

The aim of quantitative interpretation (QI) is to predict lithology and fluid content away from the well bore. This process should make use of all available data, not well and seismic data in isolation. Geological insight contributes to the selection of meaningful seismic attributes and the derivation of valid inversion products. Uncertainty must be taken into account at all stages to permit risk assessment and foster confidence in the predictions. The use of the Bayesian framework enables prior knowledge, such as a geological model, to be incorporated into a probabilistic prediction, which captures uncertainty and quantifies risk. Nostradamus is a fluid and lithology prediction toolkit that forms part of a comprehensive QI workflow. It utilises a Bayesian classification scheme to make quantitative predictions based upon inverted seismic data and depth-dependent, stochastic rock physics models. The process generates lithology and fluid probability volumes. All available information is combined using geological knowledge to create a realistic pre-drill model. Separately, stochastically modelled multidimensional crossplots, which account for the uncertainty in the rock and fluid properties (based on petrophysical analyses of well data), are used to build probability density functions such as acoustic impedance (AI) vs Vp/Vs and LambdaRho vs MuRho. These are then compared to crossplots of equivalent inverted data to make predictions and quantitatively update the geological model. Individual probability volumes as well as a most-likely lithology and fluid volume are generated. This paper presents a case study in the Carnarvon Basin that successfully predicts fluids and lithologies away from well control in a way that effectively quantifies risk and reserves. Two of the three successful gas exploration wells were drilled close to dry holes.