Unconventional reservoirs require advanced technologies such as horizontal well placement and hydraulic fracturing to be successfully exploited at economic rates. In this context, static and dynamic reservoir quality (RQ) concepts are introduced. Static RQ or standard RQ comprises a set of petrophysical parameters that describe formation tendency for development. Dynamic RQ or completion quality is defined by a set of geomechanical parameters that estimate formation tendency to be fractured. The convergence of static and dynamic RQs allows for evaluating the production potential of a field; particularly, productive sweet spots are located in those intervals in which good static and dynamic RQs are detected. We have developed a workflow to identify producible intervals in unconventional reservoirs by means of lithologic and geomechanical facies classification. Starting from core data, a clustering technique is used to create a set of lithologic facies that are then extended to the logged interval and characterized in terms of static RQ. The same approach is used to classify the logged interval with a set of geomechanical facies in which dynamic RQ is estimated. The integration of lithologic and geomechanical facies leads to sweet spot identification. Workflow application to available data from the Barnett Shale Formation allows us to classify the logged interval with four log facies (LF) and five geomechanical facies (GF) and to identify productive sweet spots in the upper and middle Lower Barnett. Eventually, LF and GF are linked to seismic facies probability volumes and Young’s modulus from elastic inversion of surface seismic. Seismic-driven geostatistical realization of LF and GF provides static and dynamic RQs volumes that are combined into volumes of productive and nonproductive facies.

In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling.

This paper presents a method for integrating information obtained from ultradeep azimuthal electromagnetic (EM) technology, and processed during geosteering activity, to update a 3D reservoir model. The latest developments in logging-while-drilling (LWD) technology, unimaginable until a few years ago, dramatically improve understanding reservoir structure far away from the wellbore. Ultradeep azimuthal EM technology provided a step change in remote detection capabilities by mapping resistivity contrasts up to tens of meters away from the wellbore. This innovation helps identify unexpected pay zones while drilling, improves subsurface understanding, and leads to well placement optimization in real time. In addition, the multiboundary reservoir mapping, provided by inversion of the ultradeep azimuthal EM measurements, allows for improvement in 3D reservoir model updates when addressing field development optimization. The method presented integrates field geological knowledge, wellbore-centric LWD data (logs and images), EM reservoir mapping information, and interpreted seismic data to refine a 3D reservoir model in the neighborhood of the well. The ultimate goal is to include the data acquired in horizontal wells in a live reservoir model update across the entire cycle of the well placement workflow. The process includes a feasibility study for technology and strategy selection, real-time geosteering execution and data integration to update the 3D reservoir model in near real time. Collaborative cross-disciplinary teams, composed of both operator and service company specialists, are focusing more and more of their attention on integrating this information into optimal field development strategy. Nowadays, it is possible for operators to handle multiboundary reservoir mapping data directly within dedicated geological modeling platforms. Advanced software solutions, designed to improve data accessibility, are the base for new integrated workflows for accurate 3D reservoir models using a multiscale dataset.

Structural estimation capability ahead of the bit is evolving with innovative combination while drilling of borehole and surface data in real time. A pioneering workflow has been developed to recalibrate the reservoir structure via integration of surface seismic with synthetic seismic, derived from logging-while-drilling (LWD) measurements. Modern LWD services have nowadays reached a significant depth of investigation capability, expanding the horizons of geosteering applications. The most recent ultradeep azimuthal electromagnetic (EM) technology provides real time information on a cylinder of rock around the wellbore, up to 200 feet of diameter. This technology enables a new opportunity to update the pre-drill 3D geo-model with the measured local volume of information. Synthetic seismic, derived from EM measurements, is compared with real seismic data, using non-rigid matching to quantify the depth mismatch. The estimated displacement is then applied to the real seismic and to the pre-drill 3D geo-model repository (i.e. identified reservoir horizons, faults, and geobodies) to predict the structural setting of the reservoir ahead of the bit. It is possible to iterate through these steps using an automated process while geosteering. The workflow was tested on post-drill data acquired on an Eni well, recently geosteered within an oil reservoir consisting of fluvial and deltaic deposits of Triassic age. The automated interpretation tools, integrated on the seismic interpretation software, allowed building a pre-drill model in two-week time. The model provided a base for the creation of the geosteering roadmap considering the structural features potentially present along the planned trajectory. The real time simulation lasted two days in a play back mode, focusing on the assessment and validation of the workflow. Each process iteration took few minutes to provide results, validated in parallel with LWD available data. The calibration provided a robust dip and structure estimation and additionally the confirmation of fluid contact position, as identified in the pre-drill model. The workflow unlocked extra look ahead possibilities for optimal geosteering, and proved to be able to provide robust information 150m, on average, ahead of the bit. The presence of structural discontinuities was successfully validated within 30 m measured depth from the predicted position. This novel approach is a step further toward the possibility of providing accurate reservoir updates ahead of the bit, and so forth to improve well placement operations while updating 3D geo-models in real time.

A workflow applied to achieve a multi-scale characterisation of a carbonate reservoir is presented. Carbonate rocks are strongly heterogeneous due either to complexity of the primary fabric or to diagenetic over-printing. The combination of these features leads to complicated pore systems, thus a proper definition of pore types using either pore size or pore throat size distributions, is important to indirectly capture diagenetic modifications and to get a link to dynamic properties. A new approach was developed in order to define a Rock Type classification (RRT) each time the approaches based on Winland's and Hydraulic Flow Unit methods do not give a reliable core facies characterisation when moving to the log scale. Moreover, the proposed workflow accounts for stratigraphy and seismic since RRT are linked to the elastic properties. In the new MICP-based Rock Typing workflow, RRT are identified by describing dominant pore types using mercury injection (MICP) curves parameterisation and routine core data (RCA). Clustering and subsequent extrapolation of MICP derived RRT to RCA samples, are the first two stages to achieve a predictable classification into the log domain. Log RRT are then defined at the log scale using curves of elastic properties, like Poisson's Ratio (PR), Frame Stiffness (fk) and Flexibility (γk) Factors. These elastic parameters (calculated with the Extended Biot Theory), can capture the effects of pore structure on the petrophysical properties and link RRT prediction at well position to seismic attributes. Since the RRT are characterised in the elastic space, the facies model – properly upscaled – represents the basis to classify elastic attributes from seismic inversion in a Bayesian framework. The seismic classification can then be used as a driver for RRT distribution in the inter-well space into the 3D model. A further benefit is the direct relationship to the original RRT porosity/permeability distributions, when modelling petrophysical properties. This new workflow was a successful solution to define homogeneous reservoir intervals in a carbonate environment characterised by the lack of a significant relationship between depositional facies and petrophysical properties.