High-resolution Geological Model Research Articles

Data-driven multiscale geomechanical modeling of unconventional shale gas reservoirs: a case study of Duvernay Formation, Alberta, West Canadian Basin

The Duvernay Formation in Canada is one of the major oil and gas source formations in the Western Canadian Sedimentary Basin, located at its deepest point. While it demonstrates promising development potential, challenges arise in the urgent need for integration of geology and engineering models, as well as in optimizing sweet spots, particularly as infill wells and pads become central operational objectives for the shale gas field. A lack of the geomechanical understanding of shale gas reservoirs presents a significant obstacle in addressing these challenges. To overcome this, we implemented data acquisition and prepared historical models and profiles, resulting in an extended high-resolution geological and reservoir property model with a fine grid system. Subsequently, a 3D full-field multi-scale geomechanical model was constructed for the main district by integrating seismic data (100 m), geological structures (km), routine logs (m), core data (cm), and borehole imaging (0.25 m), following a well-designed workflow. The predicted fracturability index (brittleness) ranges from 0.6 to 0.78, and a lower horizontal stress difference (STDIFF) is anticipated in the target formation, Upper Duvernay_D, making it a favorable candidate for hydraulic fracturing treatment. Post-analysis of the multi-disciplinary models and various data types provides guidelines for establishing a specific big database, which serves as the foundation for production performance analysis and aggregate sweet spot analysis. Fourteen geological and geomechanical candidate parameters are selected for the subsequent sweet spot analysis. This study highlights the effectiveness of multi-scale geomechanical modeling as a tool for the integration of multi-disciplinary data sources, providing a bridge between geological understanding and future field development decisions. The workflows also offer a data-driven framework for selecting parameters for sweet spot analysis and production dynamic analysis.

Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network

Large-scale or high-resolution geologic models usually comprise a huge number of grid blocks, which can be computationally demanding and time-consuming to solve with numerical simulators. Therefore, it is advantageous to upscale geologic models (e.g., hydraulic conductivity) from fine-scale (high-resolution grids) to coarse-scale systems. Numerical upscaling methods have been proven to be effective and robust for coarsening geologic models, but their efficiency remains to be improved. In this work, a deep-learning-based method is proposed to upscale the fine-scale geologic models, which can assist to improve upscaling efficiency significantly. In the deep learning method, a deep convolutional neural network (CNN) is trained to approximate the relationship between the coarse block of fine-scale hydraulic conductivity fields and the corresponding hydraulic heads, which can then be utilized to replace the numerical solvers while solving the flow equations for each coarse block. In addition, physical laws (e.g., governing equations and periodic boundary conditions) can also be incorporated into the training process of the deep CNN model, which is termed the theory-guided convolutional neural network (TgCNN). With the physical information considered, dependence on the data volume of training the deep learning models can be reduced greatly. Several cases of subsurface flow, with varying two-dimensional and three-dimensional structures and isotropic and anisotropic conditions, are used to evaluate the performance of the proposed deep-learning-based upscaling method. The results show that the deep learning method can provide equivalent upscaling accuracy to the numerical method, and efficiency can be improved significantly compared to numerical upscaling.

3-D Joint Inversion of Gravity and Magnetic Data Using Data-Space and Truncated Gauss–Newton Methods

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss–Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Application of Flow Diagnostics to Rapid Production Data Integration in Complex Grids and Dual-Permeability Models

SummaryStreamline-based methods, as repeatedly demonstrated in multiple applications, offer a robust and elegant framework for reconciling high-resolution geologic models with observed field responses. However, significant challenges persist with the application of streamline-based methods in complex grids and dual-permeability media due to the difficulty with streamline tracing in these systems. In this work, we propose a novel and efficient framework that circumvents these challenges by avoiding explicit tracing of streamlines but exploits the inherent desirable features of streamline-based production data integration in high-resolution geologic models.Our approach features the application of flow diagnostics to inverse problems involving the integration of multiphase production data in reservoir models. Here, time-of-flight as well as numerical tracer concentrations for each well, on the basis of a defined flux field, are computed on the native finite-volume grid. The information embedded in these metrics are used in the dynamic definition of stream-bundles and, eventually, in the computation of analytical water arrival-time sensitivities with respect to model properties. This calculation mimics the streamline-derived analytical sensitivity computation used in the well-established generalized travel-time inversion (GTTI) technique but precludes explicit streamline tracing. The reservoir model property field is updated iteratively by solving the LSQR (sparse least-squares with QR factorization) system composed of the computed analytical sensitivity and the optimal water travel-time shift, augmented with regularization and smoothness constraints.The power and efficacy of our approach are demonstrated using synthetic model and field applications. We first validate our approach by benchmarking with the streamline-based GTTI algorithm involving a single-permeability medium. The flow-diagnostics-derived analytical sensitivities were observed to show good agreement with the streamline-derived sensitivities in terms of correctly capturing relevant spatiotemporal trends. Furthermore, the desirable quasilinear behavior characteristic of the traditional streamline-based GTTI technique was preserved. The flow-diagnostics-based inversion technique is then applied to a field-scale problem involving the integration of multiphase production data into a dual-permeability model of a large naturally fractured reservoir. The results clearly demonstrate the effectiveness of the proposed approach in overcoming the limitations of classical streamline-based methods with dual-permeability systems. By construction, this approach finds direct application in single/multicontinuum models with generic grid designs, both in structured and fully unstructured formats, thereby aiding well-level history matching and high-resolution updates of modern geologic models.This work presents, for the first time, an application of the GTTI to dual-permeability models of naturally fractured reservoirs. This is facilitated by a simplified, yet effective approach to travel-time sensitivity computations directly on finite-volume grids. The proposed approach can be easily applied to subsurface models at levels of complexity identified as challenging for classical streamline-based methods.

A High Resolution 3D Geomodel for Giant Carbonate Reservoir- A Field Case Study from an Iraqi Oil Field

Constructing a fine 3D geomodel for complex giant reservoir is a crucial task for hydrocarbon volume assessment and guiding for optimal development. The case under study is Mishrif reservoir of Halfaya oil field, which is an Iraqi giant carbonate reservoir. Mishrif mainly consists of limestone rocks which belong to Late Cenomanian age. The average gross thickness of formation is about 400m. In this paper, a high-resolution 3D geological model has been built using Petrel software that can be utilized as input for dynamic simulation. The model is constructed based on geological, geophysical, pertophysical and engineering data from about 60 available wells to characterize the structural, stratigraphic, and properties distribution along the reservoir. Fourteen geological surfaces for all Mishrif units have been generated based on well tops data and top Mishrif structural map. The reservoir has been divided into 163 sublayers through the vertical direction and 160*383 grid cells in x-y direction with 9,988,640 total grid cells. A scale up process are performed for well log data, then, Sequential Gaussian Simulation algorithm are applied to fill 3D grid cells with properties values in areas away from wells. Pertophysical properties distribution for all reservoir zones are analyzed. The estimated initial oil in place of Mishrif through this model is close to that calculated in other previous studies.

Optimizing CO2- and Field-Gas-Injection EOR in Unconventional Reservoirs Using the Fast-Marching Method

SummaryRecently, there has been an increasing interest in enhanced oil recovery (EOR) from shale-oil reservoirs, including injection of carbon dioxide (CO2) and field gas. For the performance assessment and optimization of CO2- and field-gas-injection processes, compositional simulation is a powerful and versatile tool because of the capability to incorporate reservoir heterogeneity, complex fracture geometry, and multiphase and multicomponent effects in nanoporous rocks. However, flow simulation accounting for such complex physics can be computationally expensive. In particular, field-scale optimization studies requiring a large number of high-resolution compositional simulations can be challenging and sometimes computationally prohibitive. In this paper, we present a rapid and efficient approach for the optimization of CO2- and field-gas-injection EOR in unconventional reservoirs using a fast-marching-method (FMM) -based flow simulation.The FMM-based simulation uses the concept of diffusive time of flight (DTOF). The DTOF is a representation of the travel time of pressure-front propagation and accounts for geological heterogeneity, well architecture, and complex fracture geometry. The DTOF can be efficiently obtained by solving the Eikonal equation using the FMM. The 3D flow equation is then transformed into an equivalent 1D equation using the DTOF as a spatial coordinate, leading to orders of magnitude faster computation for high-resolution and compositional models as compared to full 3D simulations. The speed of computation enables using robust population-based optimization techniques such as genetic- or evolutionary-based algorithms that typically require a large number of simulation runs to optimize the operational and process parameters.We demonstrate the efficiency and robustness of our proposed approach using synthetic and field-scale examples. We first validate the FMM-based simulation approach using an example of CO2 huff ‘n’ puff for a synthetic heterogeneous dual-porosity model with a multistage hydraulically fractured well. Next, we present a field-scale optimization of operating strategies for gas-injection EOR in the Eagle Ford Formation. The rapid computation of the FMM-based approach enabled a comprehensive evaluation of the EOR project, including sensitivity studies, parameter-importance analysis, and optimal operating strategies using high-resolution geologic models.

Technology Update: Optimizing Well Productivity: Horizontal Logs Unlock Critical Geologic Knowledge

Technology Update With drilling on the rise, US oil production is expected to continue to increase. However, many industry professionals are concerned that the “brute force” model, which has seen a massive influx of capital flow into completion and hydraulic fracturing operations, is not an economically sustainable business model. There is a growing belief that better understanding of the effectiveness of completion designs and hydraulic fracturing strategies will require far greater subsurface geological understanding. Moreover, continued well productivity improvements will only be realized through improved reservoir characterization, and by applying formation petro-physical and geomechanical information to optimize methods of drilling, completing, and stimulating wells. There has been considerable media attention focused on how the industry is drilling wells longer and more quickly, as well as applying higher-intensity fractures. Until recently, however, there has been less emphasis on constructing productive wells with less capital. Traditional vertical well tool conveyance methods effectively addressed industry logging requirements for almost a century, but in today’s “horizontal” world, conventional methodologies are either not applicable or expensive and risky. Essentially, the multibillion-dollar logging market has not quite been turned upside down, but has toppled onto its side. Using available industry data, the low-hanging fruit in the growing “big data” boom, geosciences departments have labored to build accurate high-resolution geological models. Given the nonhomogeneous nature of the geology in unconventional reservoirs, applying vertical offset well logs and interpolating field reservoir models is generally accepted as a good start, but hardly the complete solution. To develop an accurate reservoir model, the heterogeneous nature of shale plays requires far greater data resolution than most operators appreciate. If operators are going to efficiently exploit every stage of their wellbore, they need accurate, measured formation insights along every foot of the horizontal wellbore. Geometric completions and fracturing strategies are not practical or effective in these unpredictable heterogenous environments. Along the wellbore, operators are encountering highly variable rock properties that affect fracturing efficiencies, and result in poor or unexpected well production results. This trend is forcing producers to evaluate stage-by-stage production contributions. They are finding that large portions of their wells are not contributing. There is a growing realization that improved well results that are repeatable and predictable are only going to come about by enhancing production at every stage in the wellbore. This requires engineered completions programs and tailored fracturing strategies that account for the considerable rock heterogeneity along the lateral.

Analysis of the Seismic Site Effects along the Ancient Via Laurentina (Rome)

This paper presents an evaluation of the Local Seismic Response (LSR) along the route of the ancient Roman road Via Laurentina , which has been exposed in several areas of southwest Rome over the last decade during the construction of new buildings and infrastructures. It is an example of LSR analysis applied to ancient and archaeological sites located in alluvial valleys with some methodological inferences for the design of infrastructure and urban planning. Since the ancient road does not cross the alluvial valley (namely the Fosso di Vallerano Valley) normal to its sides, it was not possible to directly perform 2D numerical modelling to evaluate the LSR along the road route. Therefore, outputs of 2D numerical models obtained along three cross sections that were normal oriented respect to the valley were projected along the route of the Via Laurentina within a reliable buffer attributed according to an available high-resolution geological model of the local subsoil. The modelled amplification functions consider physical effects due to both the 2D shape of the valley and the heterogeneities of the alluvial deposits. The 1D and 2D amplification functions were compared to output that non-negligible effects are related to the narrow shape of the fluvial valley and the lateral contacts between the lithotecnical units composing the alluvial fill. The here experienced methodology is suitable for applications to the numerical modelling of seismic response in case of linear infrastructures (i.e., roads, bridges, railways) that do not cross the natural system along physically characteristic directions (i.e. longitudinally or transversally).

Creation and delivery of a complex 3D geological survey for the Glasgow area and its application to urban geology

ABSTRACTThe Glasgow area has a combination of highly variable superficial deposits and a legacy of heavy industry, quarrying and mining. These factors create complex foundation and hydrological conditions, influencing the movement of contaminants through the subsurface and giving rise locally to unstable ground conditions. Digital geological three-dimensional models developed by the British Geological Survey are helping to resolve the complex geology underlying Glasgow, providing a key tool for planning and environmental management. The models, covering an area of 3200km2 to a depth of 1.2km, include glacial and post-glacial deposits and the underlying, faulted Carboniferous igneous and sedimentary rocks. Control data, including 95,000 boreholes, digital mine plans and published geological maps, were used in model development. Digital outputs from the models include maps of depth to key horizons, such as rockhead or depth to mine workings. The models have formed the basis for the development of site-scale high-resolution geological models and provide input data for a wide range of other applications from groundwater modelling to stochastic lithological modelling.

Streamline-based history matching of bottomhole pressure and three-phase production data using a multiscale approach

Reconciling geological models to the available dynamic information, commonly known as history matching, is an essential step for optimizing reservoir management and field development strategies, including improved recovery methods. There are several challenges in the current history matching workflow, particularly for high resolution geologic models with multimillion cells and complex geologic architecture. Streamline-based inverse modeling has shown great promise in this respect because of computational efficiency and analytic calculation of sensitivity of production response to reservoir properties. However, the current streamline-based approach has mostly been applied to history matching water-cut and tracer response in two-phase flow.More recently streamline-based sensitivity calculations have been extended to bottomhole pressure and gas-oil ratio. This allows for generalization of streamline-based history matching to the three-phase. In this paper we present an integrated application of the streamline-based three-phase history matching by incorporating water-cut, gas-oil ratio and bottomhole pressure data while updating high resolution geologic models. The crux of our approach lies in the analytic computation of well response sensitivities based on streamline which allows for efficient inversion of production and pressure data. We also validate the accuracy and efficiency of the streamline-based bottomhole pressure sensitivities by comparison with an adjoint-based method using a finite-difference simulator. In the history matching workflow, we incorporate the streamline-based method within a multiscale framework to account for the disparity in resolution of different types of history data. This integrated method proposed in this paper leads to capturing of the large- and fine-scale heterogeneity while matching the pressure and production responses efficiently.We demonstrate the power and utility of the streamline-based approach using synthetic and field applications with three phases. The synthetic example involves the SPE9 benchmark field case with waterflooding and aquifer drive. The field example involves full-field history matching of the Norne Field in the North Sea using water-cut, gas-oil ratio and bottomhole pressure data. The proposed multiscale workflow clearly demonstrates the power and advantage of our approach in achieving geologically consistent history matching at the full-field level.

Streamline-Based Time-Lapse-Seismic-Data Integration Incorporating Pressure and Saturation Effects

SummaryWe present an efficient history-matching technique that simultaneously integrates 4D repeat seismic surveys with well-production data. This approach is particularly well-suited for the calibration of the reservoir properties of high-resolution geologic models because the seismic data are areally dense but sparse in time, whereas the production data are finely sampled in time but spatially averaged. The joint history matching is performed by use of streamline-based sensitivities derived from either finite-difference or streamline-based flow simulation. For the most part, earlier approaches have focused on the role of saturation changes, but the effects of pressure have largely been ignored. Here, we present a streamline-based semianalytic approach for computing model-parameter sensitivities, accounting for both pressure and saturation effects. The novelty of the method lies in the semianalytic sensitivity computations, making it computationally efficient for high-resolution geologic models. The approach is implemented by use of a finite-difference simulator incorporating the detailed physics. Its efficacy is demonstrated by use of both synthetic and field applications. For both the synthetic and the field cases, the advantages of incorporating the time-lapse variations are clear, seen through the improved estimation of the permeability distribution, the pressure profile, the evolution of the fluid saturation, and the swept volumes.

Improved decision making with new efficient workflows for well placement optimization

Determining optimum infill well placement has been one of most challenging events in overall field development strategy of any types of field. Because there could be a large number of possible candidates for new infill well locations, it is not practically feasible to evaluate all candidate locations, particularly at high-resolution geological models including millions of geological cells. Conventional static measure maps generally used in the industry has limited applicability as this does not address dynamic fluid flow and interference between existing wells. In contrast, direct application of stochastic search optimization methods such as genetic algorithms to large-scale field models may better account for dynamically changing reservoir conditions but can be complex to apply or computationally expensive.We propose a novel method for infill well placement optimization designed as a two-stage optimization based on both dynamic flow diagnostic characteristics and genetic algorithm optimization methods. The main advantage of the proposed method over the conventional approach of using static reservoir property maps is its ability to integrate static reservoir properties with dynamic variables such as sweep efficiencies. By incorporating sweep efficiencies, new infill well potential map is generated to guide the optimization. The other benefits of this method are its maneuverability and capability to search in a narrower and tight space making the evolutionary algorithm an appropriate choice to identify rewarding locations which was not practically applicable due to the significant computational burden.The proposed automated workflow is seamlessly integrated from building a simulation model to performing reservoir simulation and conducting the optimization process. The workflow is combined with novel engineering methods to identify best rewarding well location areas, and to identify strategies to maximize sweep efficiency through optimizing well placement and completion strategies resulting in substantial engineering time-savings. The power and utility of the proposed workflow are first demonstrated by a synthetic example and then applied to a full field model. Strategies to improve incremental oil recoveries were identified with a proposed novel, iterative and robust workflow.

An integrated shear-wave velocity model for the Groningen gas field, The Netherlands

A regional shear-wave velocity (VS) model has been developed for the Groningen gas field in the Netherlands as the basis for seismic microzonation of an area of more than 1000 km2. The VS model, extending to a depth of almost 1 km, is an essential input to the modelling of hazard and risk due to induced earthquakes in the region. The detailed VS profiles are constructed from a novel combination of three data sets covering different, partially overlapping depth ranges. The uppermost 50 m of the VS profiles are obtained from a high-resolution geological model with representative VS values assigned to the sediments. Field measurements of VS were used to derive representative VS values for the different types of sediments. The profiles from 50 to 120 m are obtained from inversion of surface waves recorded (as noise) during deep seismic reflection profiling of the gas reservoir. The deepest part of the profiles is obtained from sonic logging and VP–VS relationships based on measurements in deep boreholes. Criteria were established for the splicing of the three portions to generate continuous models over the entire depth range for use in site response calculations, for which an elastic half-space is assumed to exist below a clear stratigraphic boundary and impedance contrast encountered at about 800 m depth. In order to facilitate fully probabilistic site response analyses, a scheme for the randomisation of the VS profiles is implemented.

Obtaining a high-resolution geological and petrophysical model from the results of reservoir-orientated elastic wave-equation-based seismic inversion

A previous geological and petrophysical model of the fluvio-deltaic Book Cliffs outcrops contained eight lithotypes, within each of which a number of lithologies were grouped. While this model was an adequate representation of the overall depositional architecture, for reservoir-geological purposes the potential reservoir and non-reservoir lithologies needed to be separated. Here, a new and more detailed geological model is presented in which more differentiation is put on the potential reservoir lithologies. This new model contains 12 lithologies with layers down to 1 m in thickness. Assuming a burial depth of 3 km and an average clay content, representative rock physical properties are assigned to lithologies based on published data. After the model thickness has been stretched by a factor of 4 in order to represent a more realistic reservoir, a full-waveform forward seismic response is modelled. These data are used as inputs into an iterative elastic wave-equation-based inversion scheme, with the goal to retrieve the rock properties and layer geometries. The results of this conceptual study show that sandstone units in the shoreface and distributary channels, which are potential reservoirs, are successfully identified. The recovery of medium parameters has a high resolution because the non-linear relationship between rock properties and the seismic data has been exploited.

A multi-continuum multi-phase parallel simulator for large-scale conventional and unconventional reservoirs

Reservoir simulation is considered as the core of data analysis during the development of an oilfield. With higher requirements from reservoir engineers, high-resolution geological model problems are commonly used in reservoir simulation. To simulate this kind of large-scale reservoir problems, based on black-oil models and compositional model, a parallel simulator is developed in this paper. Cartesian and corner point grids are supported by our simulator to describe complex geology, including faults and pinch-outs. The multi-continuum models (including a dual-porosity model, a dual-porosity dual-permeability model, and a multiple interacting continua model) are used to simulate fractured reservoirs. Geomechanics effects are successfully involved through a flexible and convenient interface provided by our simulator. Krylov subspace linear solvers, restricted additive Schwarz method, incomplete LU factorization (ILU) preconditioner, and a family of CPR (constrained pressure residual)-type preconditioners are implemented to provide flexibility of the linear system solution processes. Moreover, a new adaptive preconditioning strategy, which can automatically select and change preconditioner between a CPR-type preconditioner and an ILU preconditioner, is designed to accelerate the solution processes and is considered as the most efficient preconditioning technique to our knowledge. MPI-based parallel implementation is employed for each module of the simulator. With the simulator we developed, various conventional and unconventional reservoir simulation problems with large-scale geological models can be achieved on parallel computers within practical computational time. The computational efficiency and parallel scalability are achieved by our high-quality parallel implementation and efficient nonlinear and linear solution techniques.

Streamline-Based History Matching for Multicomponent Compositional Systems

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 174750, “Streamline-Based History Matching of Arrival Times and Bottomhole-Pressure Data for Multicomponent Compositional Systems,” by Shusei Tanaka, Dongjae Kam, Akhil Datta-Gupta, and Michael J. King, Texas A&M University, prepared for the 2015 SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. The paper has not been peer reviewed. Streamline-based history-matching techniques have provided significant capabilities in integrating field-scale water-cut and tracer data into high-resolution geologic models. However, application of the streamline-based approach for simultaneous integration of water cut and bottomhole pressure (BHP) has been rather limited. In this paper, the authors introduce a novel semianalytic approach to compute the sensitivity of the BHP data with respect to gridblock properties. Introduction The streamline-based method has many advantages in terms of computational efficiency and applicability. The main advantage of the streamline-based method is that it is able to calculate parameter sensitivity with a single streamline simulation or post-processing of the grid-based finite-difference simulation results. The calculated sensitivity is comparable with the sensitivities computed from numerical perturbation or with adjoint-based sensitivities. It is possible to calculate the parameter sensitivity from a commercial finite-difference simulator by use of streamlines traced using the flux field. This allows accounting for detailed physics by means of finite-difference simulation while taking advantage of the power of the streamlines for sensitivity computations. Streamline and Parameter Sensitivity Streamline-based history matching starts with the tracing of streamlines from a given set of static and dynamic conditions. This process is outlined in detail in the complete paper. Amplitude, Travel-Time, and Generalized Travel-Time Inversion (GTTI) Methods The approach to minimizing the objective function through all the observed points and simulation results is defined as amplitude inversion. The travel-time inversion, instead, attempts to match single reference times such as water-breakthrough point or peak tracer response. The amplitude matching is general in terms of reduction of the objective function, because while the travel-time inversion reduces the objective function at only a single point, the amplitude matching can cover all the data points. However, the travel-time-based approach is often used because amplitude inversion is a highly nonlinear problem and presents difficulties in computational cost and in reducing the objective function when there is a large amount of wells and data points.

The local seismic response of the Fosso di Vallerano valley (Rome, Italy) based on a high-resolution geological model

The main purpose of this study is the analysis of the local seismic response in the Fosso di Vallerano valley, an alluvial basin located in a recently urbanized area of Rome (Italy). A high-resolution geological model was obtained starting from 250 available borehole log stratigraphies and pressiometer in-site tests; the model highlighted a complex and heterogeneous setting of both the local substratum and the alluvial fill. The local seismo-stratigraphy was derived based on several noise measurements as well as one cross-hole test. A preliminary validation of the stratigraphic model was performed by modeling the amplification functions (SSR) derived from weak motions recorded on August 2009 through a temporary velocimetric array. The obtained results show that the recent alluvial deposits have one principal mode of vibration at about 0.8 Hz and different secondary vibrational modes due to the characteristic stratigraphic setting of the alluvia. The seismic bedrock can be located within the Paleo Tiber 4 deposits (Santa Cecilia Formation) by assuming a velocity gradient in these deposits (i.e. corresponding to the Vs increment from about 500 m/s up to 1100 m/s).

High-resolution geological model of the gravitational deformation affecting the western slope of Mt. Epomeo (Ischia)

The recent geological history of Ischia Island is characterized by slope-scale gravitational deformations closely related to volcano-tectonic dynamics of the Mt. Epomeo resurgent caldera. This study focuses on the gravitational deformation that involves alkali-trachytic lava and trachytic ignimbrite flow-units of Mt. Nuovo, located in the western portion of Mt. Epomeo. A preliminary, high-resolution engineering-geological model was obtained through geological, geomorphological and geophysical surveys and reveals a complex morpho-structure with geomorphological evidence of gravitational instability. The complexity of the ongoing slope deformations is confirmed by field geo-structural evidences that led to the identification of a multiple compound mechanism with a main rupture surface which is about 200 m deep. This geometry was better constrained by passive seismic investigations consisting in noise measurements, focused on resonance frequencies of the soil (i.e. based on H/V Nakamura approach). In addition, a close relationship between the outcrop of Mt. Epomeo Green Tuff breccia layers and the distribution of hydrothermal emissions and gas vent can be inferred, as it is related to the higher permeability of the breccia layers with respect to the main Mt. Epomeo Green Tuff flow unit, where the ascent path of deep hydrothermal fluids developed along faults and fracture networks.

Time-Lapse Seismic Data Integration Incorporates Pressure and Saturation Effects

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 166395, ’Streamline-Based Time-Lapse Seismic-Data Integration Incorporating Pressure and Saturation Effects,’ by Shingo Watanabe, SPE, Jichao Han, SPE, Akhil Datta-Gupta, SPE, and Michael J. King, SPE, Texas A&M University, prepared for the 2013 SPE Annual Technical Conference and Exhibition, New Orleans, 30 September-2 October. The paper has not been peer reviewed. This paper presents an efficient history matching approach that simultaneously integrates 4D repeat seismic surveys with well production data. This approach is particularly well-suited for the calibration of the reservoir properties of high-resolution geologic models because the seismic data are areally dense but sparse in time, while the production data are continuous in time but averaged over interwell spacing. The joint history matching is performed using streamline-based sensitivities derived from either finite-difference or streamline-based flow simulation. Introduction Previous studies have made attempts at quantitative 4D seismic history matching. However, the reconciliation of reservoir model heterogeneity with temporal changes in seismic attributes remains a particularly complex task. Several dynamic data integration algorithms have been proposed in the literature, which can be categorized broadly into three data integration levels: (1) reservoir-simulation- based integration between the pressure and saturation estimates inverted from seismic observation data and the direct simulated saturation and pressure responses, (2) petroelastic-based integration between the seismic inverted rock elastic properties derived from a geophysical inversion and the simulated rock elastic responses from the simulated saturation and pressure responses by means of petroelastic models, and (3) seismic-forward-model-based integration between the direct seismic traces attributes and the simulated seismic responses by means of seismic wave propagation modeling. The seismic-forward-model-based seismic data integration approach uses direct seismic trace data and circumvents the uncertainties associated with the seismic-inversion process. The general workflow for generating synthetic seismic data from a reservoir simulation model generally involves (1) static reservoir properties on the simulation grid, (2) simulation of dynamic pressure and fluid saturations at each cell, (3) computation of seismic elastic properties, and (4) simulation of the seismic attribute by applying a seismic wave propagation model over the reservoir interval and the overburden rock. This study examines both reservoir-simulation- based calibration and rock-physics- based calibration. In the first instance, the authors calibrate against time-lapse changes in both pressure and saturation. In the second instance, they calibrate against the time-lapse change in acoustic impedance. In both cases, the inversion will provide local updates to the reservoir model using analytic streamline- based sensitivity calculations.

Modelling and simulation of a Jurassic carbonate ramp outcrop, Amellago, High Atlas Mountains, Morocco

Carbonate reservoirs pose significant challenges for reservoir modelling and flow prediction due to heterogeneities in rock properties, limits to seismic resolution and limited constraints on subsurface data. Hence, a systematic and streamlined approach is needed to construct geological models and to quickly evaluate key sensitivities in the flow models. This paper discusses results from a reservoir analogue study of a Middle Jurassic carbonate ramp in the High Atlas Mountains of Morocco that has stratigraphic and structural similarities to selected Middle East reservoirs. For this purpose, high-resolution geological models were constructed from the integration of sedimentological, diagenetic and structural studies in the area. The models are approximately 1200×1250 m in size, and only faults (no fractures) with offsets greater than 1 m are included. Novel methods have been applied to test the response of flow simulations to the presence or absence of specific geological features, including proxies for hardgrounds, stylolites, patch reefs, and mollusc banks, as a way to guide the level of detail that is suitable for modelling objectives. Our general conclusion from the study is that the continuity of any geological feature with extreme permeability (high or low) has the most significant impact on flow.