A B S T R A C TUnderstanding and predicting reservoir presence and characteristics at regional to basin scales is important for evaluating risk and uncertainty in hydrocarbon exploration. Simulating reservoir distribution within a basin by a stratigraphic forward model enables the integration of available prior information with fundamental geologic processes embedded in the numerical model. Stratigraphic forward model predictions can be significantly improved by calibrating the models to independent constraints, such as thicknesses from seismic or well data. A three-dimensional basin-scale stratigraphic forward-modeling tool is coupled with an inversion algorithm. The inversion algorithm is a modification of the neighborhood algorithm (a type of genetic algorithm), which is designed to sample complex multimodal objective functions and is parallelized on computer clusters to accelerate convergence. The process generates a set of representative geological models that are consistent with prior ranges for uncertain parameters, calibration constraints, and associated tolerance thresholds. The workflow is first demonstrated on two data sets: a synthetic example based on a clastic passive margin and a real hydrocarbon exploration example for slope and basin-floor stratigraphic traps in the Neocomian (Lower Cretaceous) of the West Siberian Basin. The analysis of calibrated models provides constraints on stratigraphic controls, and allows prediction of locations with higher potential to develop stratigraphic traps. These locations are related to complex interactions between paleobathymetry, subsidence, eustatic fluctuations, characteristics of sediment-input sources, and sediment-transport parameters. Results show the potential of stratigraphic forward modeling O. Falivene joined Shell in 2008, and since then he has worked for research and development (R&D) developing and applying stratigraphic forward models to support worldwide exploration in clastic and carbonate settings. Oriol has a background in stratigraphy, geocellular modeling, and geostatistics (Ph.D. in modeling outcrop analogs at the University of Barcelona, Spain), and a short period working on reservoir modeling for British Petroleum.A. Frascati ∼ Shell Global Solutions International B.V.,

SUMMARYThree‐dimensional (3‐D) images of osteocyte lacunae were examined on a confocal microscope. Both geostatistical and morphological processing techniques were used to improve and to analyse them. By a geostatistical approach, this study aims at improving 3‐D confocal images before any further image processing. Optimized linear filters, which take account of the second‐order statistics and the 3‐D structure of the data, allow for the removal of imperfections such as noise and/or blur due to the axial convolution, and interpolate voxels on a face‐centred cubic grid from an initial cubic grid. An application of this technique to 3‐D biological images is demonstrated. In a second step, a 3‐D binary image is digitized and cleaned with 3‐D morphological filters. The standard 3‐D measurements cannot be applied in this case, since all osteocytes cut the border of the field. For this reason a 3‐D Boolean model has been adjusted, from which it is possible to derive all useful information on the repartition and the morphology of the osteocytes.

This paper addresses the issue of the sensitivity of 3‐D prestack depth migration (PSDM) with respect to the acquisition geometry of 3‐D seismic surveys. Using the theoretical framework of PSDM, I show how acquisition‐related imaging artifacts—the acquisition footprints—can arise. I then show how the acquisition footprint can be suppressed in two steps by (1) partitioning the 3‐D survey into minimal data sets, each to be migrated separately, and (2) applying a robust variable‐geometry PSDM quadrature. The validity of the method is demonstrated on synthetic parallel and antiparallel multistreamer data and cross‐spread data. The proposed two‐step solution can play an important role in projects where amplitude integrity and fidelity are paramount, e.g., quantitative interpretation and time‐lapse surveying. The concept of minimal data also fills a gap in understanding the relation between acquisition and imaging.

The application of sequence stratigraphy to seismic interpretation has proven to be fundamentally important in basin analysis. It provides a framework for understanding strat-igraphic evolution and is a key element in predicting the spatial distribution of reservoir, seal, and source rocks. Traditional methods of seismic se-quence stratigraphy make use of observations such as stacking patterns, seismic character of facies, and their distribution to develop subsurface models. We present a set of seismically derived geometric attributes that enhance and characterize these observations, allowing a sequence stratigraphic framework to be developed in the earliest stages of interpretation.

Using inverted seismic data from a turbidite depositional environment, we have determined that accounting only for rock types sampled at the wells can lead to biased predictions of the reservoir fluids. The seismic data consisted of two volumes resulting from a (multi-incidence angle) sparse-spike amplitude variation with offset inversion. Information from a single well (well logs and petrological analysis) was used to define an initial set of lithofluid facies that characterized rock type and porefill fluid to emulate a typical exploration setting. Based on our geologic understanding of the study area, we have augmented this initial model with lithofluid facies expected in the given depositional environment, yet not sampled by the well. Specifically, the new lithofluid facies accounted for variations in the mixture type and proportions of shales and sands. The elastic property distributions of the new lithofluid facies were modeled using appropriate rock-physics models. Finally, a geologically consistent, spatially variant, prior probability of lithofluid facies occurrence was combined with the data likelihood to yield a Bayesian estimation of the lithofluid facies probability at every sample of the inverted seismic data. Applying the augmented geologic prior probabilities, we were able to generate a scenario consistent with all available data, which supports further development of the field. In contrast, using the initial, purely data-driven lithofluid facies model based on a single well, the Bayesian classification would lead to prospectivity downgrade or suboptimal development of the field. We found that limited well control in quantitative interpretation needs to be counterweighted by geologic prior information based on detailed stratigraphic interpretation, to derisk geologic scenarios without bias.

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Deep learning is increasingly being used as a component of geoscience workflows for processing and interpreting seismic data. Training a supervised deep learning network is a data-hungry task: Lots of data examples are needed and they must include labels. The data examples and their labels must have consistent patterns for the deep learning network to learn. Too few examples and/or poor-quality labels can lead to poor deep learning training results. One method to provide large quantities of training examples with high-quality labels is to create synthetic data. We discuss our techniques and experiences with our ongoing use of synthetic seismic data. We share our techniques as an open-source project concurrent with this paper at https://github.com/tpmerrifield/synthoseis. We hope that the geoscience community will share our enthusiasm for developing deep learning geoscience tools and for including synthetic seismic data in supervised deep learning training. We invite contributions from the geoscience community using the open-source model to collectively reduce the realism gap between synthetic data and field seismic data.

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