Integration Of Dynamic Data Research Articles

Integration of static and dynamic reservoir data to optimize the generation of subsurface fracture map

This paper presents a new three-dimensional fully coupled poroelastic numerical model to simulate pressure transient response of naturally fractured reservoirs. One of the main applications of the new approach is to improve the reservoir characterization by decreasing the uncertainties associated with subsurface fracture map and to understand the interaction between fracture and matrix. The model is based on a hybrid methodology to simulate fluid flow by combining a single continuum and discrete fracture network approaches. To decrease the uncertainty associated with subsurface fracture map, a different realizations of the discrete fracture systems are generated based on image logs, seismic, geological features and the tectonic history of the studied reservoir. An iterative loops have been used between the reservoir static model and dynamic well-test data to optimize the generation of subsurface fracture maps. At each loop, a new discrete fracture system is used and the simulated pressure transient data are compared with the available field data. The process is repeated till the matching between the simulated and the measured pressure transient data is achieved using the most appropriate fracture realization. The numerical model is validated against an analytical pressure transient solution for dual-porosity system, and then applied to a real case taken from fractured basement reservoir in offshore Southern Vietnam. The developed hybrid methodology used to simulate fluid flow and rock deformation to improve the characterization of the fractured basement by simulating the pressure transient testing. The results predicted by the presented model are in a good agreement with field data; also the model was able to predict the fractures network distribution and fractures inherent properties around the tested well.

A position-aware Merkle tree for dynamic cloud data integrity verification

In the cloud storage framework, once clients remotely store their data on cloud storage providers, they will lose the physical control over their outsourced data. The risk of unauthorized access to the data increases dramatically. One of the most serious problems in cloud storage is to ensure the correctness of the outsourced data. Specifically, we need to protect these data from unauthorized operations; we also need to detect and recover users' data after unexpected changes. In this paper, we propose a publicly verifiable scheme to protect the integrity of cloud data and support dynamic maintenance, which is based on a position-aware Merkle tree. We adopt a 3-tuple to define the node of the new Merkle tree, which records the position of the corresponding node, so that users can verify the consistency of the challenge-response blocks by computing the root value directly without retrieving the whole Merkle tree. In our scheme, the storage complexity at the client side is O(1); the computation complexity at the client side is $$O(\log n)$$O(logn); the computation cost at the server side is $$O(\log n)$$O(logn) and the communication overhead is $$O(\log n)$$O(logn). Our method supports unlimited verification challenges as well.

Uncertainty Quantification Using Streamline Based Inversion and Distance Based Clustering

For decision making, it is crucial to have proper reservoir characterization and uncertainty assessment of reservoir performances. Since initial models constructed with limited data have high uncertainty, it is essential to integrate both static and dynamic data for reliable future predictions. Uncertainty quantification is computationally demanding because it requires a lot of iterative forward simulations and optimizations in a single history matching, and multiple realizations of reservoir models should be computed. In this paper, a methodology is proposed to rapidly quantify uncertainties by combining streamline-based inversion and distance-based clustering. A distance between each reservoir model is defined as the norm of differences of generalized travel time (GTT) vectors. Then, reservoir models are grouped according to the distances and representative models are selected from each group. Inversions are performed on the representative models instead of using all models. We use generalized travel time inversion (GTTI) for the integration of dynamic data to overcome high nonlinearity and take advantage of computational efficiency. It is verified that the proposed method gathers models with both similar dynamic responses and permeability distribution. It also assesses the uncertainty of reservoir performances reliably, while reducing the amount of calculations significantly by using the representative models.

Using geological well testing for improving the selection of appropriate reservoir models

Analytical well-test solutions are mainly derived for simplified and idealized reservoir models and therefore cannot always honour the true complexity of real reservoir heterogeneities. Pressure transients in the reservoir average out heterogeneities, and therefore some interpretations may not be relevant and could be misleading. Geological well testing refers to the numerical simulation of transient tests by setting up detailed geological models, within which different scales of heterogeneity are present. The concept of geological well testing described in this paper assists in selecting from multiple equi-probable static models. This approach is used to understand which heterogeneities can influence the pressure transients. In this paper, a low-energy multi-facies fluvial reservoir is studied, for which data from a well test of exceptionally long duration are available. The pervasive low reservoir quality facies and restricted macro cross-flow between the reservoir layers give rise to an effective commingled system of flow into the wellbore (i.e. zero or very low vertical cross-flow between the reservoir units). In our model, facies transitions produce lateral cross-flow transients that result in a ‘double-ramp-effect’ signature in the test response. A sophisticated multi-point statistical (MPS) facies modelling approach is utilized to simulate complex geological heterogeneities and to represent facies spatial connectivity within a set of generated static models. The geological well-test model responses to a real well-testing cycle are then evaluated using dynamic simulation. The pressure match between simulated and recorded data is improved by generating multiple facies and petrophysical realizations, and by applying an engineering-based hybridization algorithm to combine different models that match particular portions of the real well-test response. In this example, the reservoir dynamics are controlled by subtle interaction between high-permeability channels and low-permeability floodplain deposits. Effective integration of geology and dynamic data using modern methods can lead to better reservoir characterization and modelling of such complex reservoir systems.

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.

Ontology-based Query Processing in a Dynamic Data Integration System

Data integration is concerned with unifying data that share some common semantics but originate from unrelated sources. Necessarily, when we work on data integration, we must take into account a more important and complex concept called “heterogeneity”. We begin by introducing Data integration systems (DIS), Ontologies and other preliminary concepts that will be used throughout the presentation. We will discuss how can Ontologies support, Integration? Also about the Ontologies and integration problems. We define architecture used for Semantic Query Distribution and we formally discuss the problem of query rewriting inside our data integration framework. Finally, we close this paper illustrating the current problem future work.

Reducing uncertainty in calibrating aquifer flow model with multiple scales of heterogeneity.

Modeling and calibration of natural aquifers with multiple scales of heterogeneity is a challenging task due to limited subsurface access. While computer modeling plays an essential role in aquifer studies, large uncertainty exists in developing a conceptual model of an aquifer and in calibrating the model for decision making. Due to uncertainties such as a lack of understanding of subsurface processes and a lack of techniques to parameterize the subsurface environment (including hydraulic conductivity, source/sink rate, and aquifer boundary conditions), existing aquifer models often suffer nonuniqueness in calibration, leading to poor predictive capability. A robust calibration methodology is needed that can address the simultaneous estimations of aquifer parameters, source/sink, and boundary conditions. In this paper, we propose a multistage and multiscale approach that addresses subsurface heterogeneity at multiple scales, while reducing uncertainty in estimating the model parameters and model boundary conditions. The key to this approach lies in the appropriate development, verification, and synthesis of existing and new techniques of static and dynamic data integration. In particular, based on a given set of observation data, new inversion techniques can be first used to estimate aquifer large-scale effective parameters and smoothed boundary conditions, based on which parameter and boundary condition estimation can be refined at increasing detail using standard or highly parameterized estimation techniques.

Rapid quantification of uncertainty in permeability and porosity of oil reservoirs for enabling predictive simulation

One of the most difficult tasks in subsurface flow simulations is the reliable characterization of properties of the subsurface. A typical situation employs dynamic data integration such as sparse (in space and time) measurements to be matched with simulated responses associated with a set of permeability and porosity fields. Among the challenges found in practice are proper mathematical modeling of the flow, persisting heterogeneity in the porosity and permeability, and the uncertainties inherent in them. In this paper we propose a Bayesian framework Monte Carlo Markov Chain (MCMC) simulation to sample a set of characteristics of the subsurface from the posterior distribution that are conditioned to the production data. This process requires obtaining the simulated responses over many realizations. In reality, this can be a prohibitively expensive endeavor with possibly many proposals rejection, and thus wasting the computational resources. To alleviate it, we employ a two-stage MCMC that includes a screening step of a proposal whose simulated response is obtained via an inexpensive coarse-scale model. A set of numerical examples using a two-phase flow problem in an oil reservoir as a benchmark application is given to illustrate the procedure and its use in predictive simulation.

Ensuring Dynamic Data Integrity based on Variable Block-size BLS in Untrusted Environment

Security becomes the major concern when people utilizing cloud computing. One major challenge is to ensure the integrity of data in untrusted environment under the condition of supporting public auditability and dynamic data operation. The paper proposes an improved data integrity verifying protocol to ensure the security of user’s data and promote the verifying efficiency using dynamic Merkle tree with relative index. The protocol is based on an extended security model for untrusted environment where each participant may act as an untrusted role. Furthermore, because BLS based schemes have the advantages of shorter homomorphic signatures than RSA based schemes but with disadvantage of fixed and small block-size, a BLS based scheme with variable block-size is integrated in the protocol. Performances analysis showed that the improved protocol is effective and provides a practical and flexible solution for untrusted environment. The computation complexity of dynamic data operation and integrity verifying reaches O(log n) definitely.

Integration of dynamic rainfall data with environmental factors to forecast debris flow using an improved GMDH model

The objective of this study was to apply an improved Group Method of Data Handling (GMDH) network model for prediction of debris flow by integrating dynamic rainfall data and environmental factors. The rainfall data were collected from weather information, and the environmental data were extracted from RS, GIS, drilling data, and geophysical data. The input variables used in the SAGA-GMDH model were derived from six variables acquired by Kernel Linear Discriminant Analysis (KLDA). The results showed that the GMDH for prediction of debris flow performed well using the training, validation, and testing sets (R2 above 0.80 and ARE below 3.54%). The SAGA-GMDH model was subsequently compared with a back-propagation (BP) neural network model and adaptive network fuzzy interference system (ANFIS). The accuracies of the SAGA-GMDH model prediction were slightly better than those of other two models, which demonstrated that the SAGA-GMDH model was more suitable for prediction of debris flow.

Data fusion-based risk assessment framework: an example of benzene

Environmental and health risk assessment projects involve processing of a large volume of uncertain information. To reduce uncertainties in the assessment process, the risk assessor evaluates a large volume of data from toxicological, exposure and health related databases. Being a data intensive assessment, for improved risk assessment, there is a need for these data to be harmonized and integrated by incorporating information from various sources and biological organizational criteria (e.g., gene, cellular, tissue, organ level health effects, if available). The main objective of this study is to have a front end or upstream approach towards an effective dynamic data fusion (DF)-based risk assessments. To achieve this goal, a generalized human health risk assessment and a system biology-based DF framework have been proposed. The proposed approach would be able to detect different trends and patterns and integrates various toxicological datasets from different biological organizational criteria (e.g., gene, cellular, tissue, organ level, if available) and integrates data from disparate sources. To demonstrate the effectiveness of the approach, both the proposed frameworks have been implemented in human health risk assessment for benzene originated from an illustrative example of a contaminated site. The application in this study shows that this approach can increase the efficiency of dynamic data integration and incorporation of toxicity pathway or system biology-based information in the human health risk analysis. Currently, the system biology facilitated DF framework is based on hypothesis driven modeling relationships between different bio-organizational criteria. Further work is needed to prove these relationships. For a data rich chemical example such as benzene, these relationships can be established based on the existing and the emerging knowledge-base on benzene toxicology.

Image processing tools for better incorporation of 4D seismic data into reservoir models

Reservoir engineers have to predict the behavior of a hydrocarbon reservoir by building a simulation model which can best reproduce the data collected in the field. These data fall into two types: static data, which are invariable in time, and dynamic data, which evolve according to fluid motions in the reservoir. In this paper, we focus on the integration of dynamic data related to four-dimensional (4D) inverted seismic data. Such seismic data constitute an invaluable source of information on fluid displacement and geology over extensive areas of the reservoir. However, incorporating them in the reservoir model through a matching process is a challenging task. Classical formulations of the objective function, which computes the misfit between observed data and responses computed by the reservoir model, are not adapted to 4D inverted seismic data. For example, a least square based mismatch is not representative of the visual difference between two seismic images. In this paper, we define a new formulation of the objective function based on simplification of seismic data in order to extract relevant information. This simplification involves filtering and segmentation techniques, as well as image comparison methods rooted in image analysis. More precisely, we focus on the non-local means algorithm for filtering, on the level-set framework for segmentation and on the local modified Hausdorff distance for image comparison. We investigate the efficiency of such techniques in the context of seismic data, and illustrate their potential on a synthetic history matching reservoir example.

A GPS Analysis for Urban Freight Distribution

Daily life in urban centers has led to increasing and more demanding freight requirements. Manufacturers, retailers and other urban agents have thus tended towards more frequent and smaller deliveries, resulting in a growing use of light freight vehicles (<3.5 ton). This paper characterizes and analyzes urban freight distribution in order to generate new ways of understanding the phenomenon. Based on a case study of two different-sized Spanish cities using data from GPS, a vehicle observation survey and complementary driver's interviews, the authors propose a categorization of urban freight distribution. The results confirm GPS as a useful tool that allows the integration of dynamic traffic assignment data and diverse traffic operation patterns during different day periods, thereby improving delivery performance.

An Architecture for Managing Knowledge and System Dynamism in the Worldwide Sensor Web

Sensor Web researchers are currently investigating middleware to aid in the dynamic discovery, integration and analysis of vast quantities of both high and low quality, but distributed and heterogeneous earth observation data. Key challenges being investigated include dynamic data integration and analysis, service discovery and semantic interoperability. However, few efforts deal with managing knowledge and system dynamism. Two emerging technologies that have shown promise in dealing with these issues are ontologies and software agents. This paper presents an integrated ontology driven agent based Sensor Web architecture for managing knowledge and system dynamism. An application case study on wildfire detection is used to illustrate the operation of the architecture.

Dynamic Data Integration and Quantification of Prediction Uncertainty Using Statistical-Moment Equations

Summary The use of a probabilistic framework for dynamic data integration (history matching) has become accepted practice. In this framework, one constructs an ensemble of reservoir models, in which each realization honors the available (static and dynamic) information. The variations in the flow performance across the ensemble provide an assessment of the prediction uncertainty due to incomplete knowledge of the reservoir properties (e.g., permeability distribution). Methods based on Monte Carlo simulation (MCS) are widely used because of the generality and simplicity of MCS. As a black-box approach, only pre- and post-processing of conventional flow simulations are needed. To achieve reasonable accuracy in estimating the statistical moments of flow-performance predictions, however, large numbers of realizations are usually necessary. Here, we use a different, and direct, approach for model calibration and uncertainty quantification. Specifically, we describe a statistical-moment-equations (SMEs) framework for both the forward and inverse problems associated with immiscible two-phase flow. In the SME method, the equations governing the statistical moments of the quantities of interest (e.g., pressure and saturation) are derived and solved directly. We assume that statistical information and a few measurements are available for the permeability field. As for the dynamic properties, we assume that measurements of pressure, saturation, and flow rate are available at a few locations and at several times. For the forward problem, the flow (pressure and total-velocity) SMEs are solved on a regular grid, while a streamline-based strategy is used to solve the transport SMEs. We use a kriging-based inversion algorithm, in which the first two statistical moments of permeability are conditioned directly using the available dynamic data. We analyze the behaviors of the saturation moments and their evolution as they are conditioned on measurements, in both space and time. Moreover, we discuss the relationship between the widely used MCS-based Kalman-filter approach and our SME inversion scheme.

Cosimulation as a perturbation method for calibrating porosity and permeability fields to dynamic data

The geological characterization of formations such as reservoirs or underground storage sites entails the description of the spatial distribution of porosity/permeability properties on the basis of the static and dynamic data collected. The integration of dynamic data can be expressed as an inverse problem: the purpose is to identify a set of porosity/permeability values which allows for reproducing the available data. This issue motivated the development of geostatistical-based parameterization techniques, which make it possible to vary the spatial distribution of porosity/permeability values within the geological model from a reduced number of parameters while preserving their spatial variabilities. This paper introduces first an algorithm for performing cosimulation, then a new method for continuously modifying realizations representing porosity or permeability fields. This one consists of simulating cross-correlated Gaussian random functions with identical means and covariances. The realizations resulting from the cosimulation process depend on the linear correlation coefficients between the random functions considered. Variations in these correlation coefficients induce variations in the realizations. Then, the proposed cosimulation perturbation method is incorporated into a history-matching process to calibrate porosity and permeability fields to dynamic data. The parameters to be adjusted are the various correlation coefficients. Last, an example is shown to stress the potential of the method.

Fully Integrated Hydrocarbon Reservoir Studies: Myth or Reality?

Problem statement: In the petroleum industry and especially during reservoir studies, data coming from different disciplines must be combined in order to generate a model that is representative of the reservoir being studied and can be used for defining the most viable development strategy of the field from both an economic and technical standpoint. Each of these disciplines represents an independent piece of a puzzle that is solved by professionals from various scientific fields who have different educational backgrounds. Integration among geophysics, geology, fluid dynamics and geomechanics is truly essential, but requires specific approaches and procedures for generating and calibrating a reservoir model capable of dealing with all and each of these aspects. Approach: Independent workflows were examined for each of the disciplines involved so as to highlight unavoidable interdependencies between static, dynamic and geomechanical models, even when the goal is to tackle each issue separately. Then, the traditional working method was compared to the integrated approach that supports the generation and calibration of models based on data and interpretation results from all the disciplines involved in the entire project. Results: The construction of a reservoir model should be regarded as a dynamic process, subject to repeated updates as new data is made available and by frequent modifications when inconsistencies are found between the understanding that different specialists have of the same system. This approach has exhibited great advantages in terms of improvement in the quality and flexibility of the model, reduction of working time and generation of a single final model that can be adapted or used for any kind of simulation problem. Conclusion: An integrated approach is necessary for reservoir modeling purposes. Modern reservoir studies should be designed accordingly to permit the full integration of static, dynamic and geomechanical data into a single reservoir model. Integration is always beneficial, even though there still remains a misconception that it is not needed at all times. For this reason, it is recommended that an effort is made to set up a model capable to handle all aspects of a reservoir study each time a new field study is undertaken, even when it is not envisioned that all aspects might be of interest in the future.

Integrated approach to geomodelling and dynamic simulation in a complex mixed siliciclastic–carbonate reservoir, N'Kossa field, Offshore Congo

Abstract The Lower Sendji Carbonate (Albian age) of the N'Kossa field is a mixed siliciclastic–carbonate reservoir exhibiting very heterogeneous reservoir properties and the development of extensive vertical flow permeability barriers. The reservoir is a succession of interstratified dolomite, limestone, sandstone and shale lithologies of sabkha, tidal flat and lagoonal origin. Early, synsedimentary dolomite cements are extensively developed, particularly over local palaeohighs. Challenges to hydrocarbon production include: (1) the vertical variability of reservoir properties; (2) the presence of many, laterally extensive vertical flow barriers; and (3) variable connectivity between fault-bounded compartments. This complexity was underestimated at the appraisal stage, the initial development plan calling for a relatively simple scheme of pressure support to the critical reservoir fluid via injection into the gas cap and the water leg. This decision was supported by the identification of a single hydrocarbon column (&gt;400 m thick) over the entire structure which suggested a lack of vertical compartmentalization. Recent studies, which include a geological synthesis of more than forty wells and a dynamic data synthesis to determine the production mechanism and identify key heterogeneities, show a lack of pressure support to the oil leg from both water and gas injector wells. Today, integration of static and dynamic data together with an improved geological understanding of the stratigraphic control on vertical flow barriers and mapping of high permeability sandstone layers is the key to identifying unswept zones of the field and future infill drilling targets.

The Buzzard Field: anatomy of the reservoir from appraisal to production

Abstract The Buzzard oil field in the Outer Moray Firth, Central North Sea was discovered in 2001 and rapidly appraised during 2001–2002. Pre-production development drilling began in 2005 and the field was brought on stream in January 2007 by operator Nexen Petroleum UK Ltd. The Buzzard reservoir consists of Upper Jurassic deep marine turbidites within the Kimmeridge Clay Formation. Sands were derived from the shelf to the west and the resulting mass flow deposits were contained within a fault-bounded basin, whose margins were prone to mass wastage. The deposits pinchout to the west and thicken eastwards into the basin and hydrocarbon source kitchen. Following appraisal drilling, the high net-to-gross reservoir was interpreted as well connected. Interbedded shale layers were thought unlikely to control flow through the reservoir. However, a biostratigraphic review indicated that a consistent biostratigraphic event, indicative of reworking, could be identified field-wide. This shale layer is interpreted as a large-scale, muddy slump, extensive enough to form a significant vertical barrier to flow. Subsequent production data, modelling and chemostratigraphy studies suggest greater subdivision and vertical baffling within the main reservoir. Interpretation of these data has led to a move away from early models of Buzzard as a tank-like reservoir to a model dominated by compensationally stacked lobes, where hydraulic flow is influenced by sand body geometry and extensive shales. A comprehensive understanding of the Buzzard Field has only been possible through full integration of core, log and dynamic data, benefiting from a high density of well control, an extensive data acquisition programme and early reservoir monitoring. Ongoing reservoir management, involving continuous update of both the geological and dynamic models in response to new data, has enabled an evolving understanding of the Buzzard reservoirs, and placed the operator in an excellent position to proactively address future development challenges.

Turbidite reservoir compartmentalization and well targeting with 4D seismic and production data: Schiehallion Field, UK

Abstract This paper discusses integration of production surveillance techniques, focusing on the use of 4D seismic data to identify reservoir compartmentalization. We present two examples of recently drilled compartments that were successfully identified following integration of surveillance data with detailed reservoir modelling work. Our examples are from the faulted, Paleocene channelized turbidite reservoirs of the Schiehallion oil field, offshore West of Shetlands, U.K. The first example provides a good case history of a 4D Integrated Reservoir Modelling (4D IRM) approach – which involved integration of dynamic well data with 4D seismic, and iterative revision of geological and reservoir simulation models. Two newly identified targets were drilled and completed successfully using these techniques. The second example illustrates a situation where 4D seismic interpretation was key in identifying a new infill target. Production in the Schiehallion Field started in 1998, and the current development scheme totals 46 wells (22 producers and 24 water injectors). During the early years of production it became apparent that geological connectivity, fluid flow and pressure communication between wells was not as inter-connected as expected. As a result the number of wells required to maximize the recovery has more than doubled to that specified in the original development plan, and the number is expected to increase further as the field matures. Continuous collection of bottom hole pressure data from permanently installed gauges, well testing and production logging (PLT) supported by a regular time-lapse (4D) seismic programme are used to update conceptual thinking and thus constrain geological and flow simulation modelling. This data integration results in improved understanding of the static and dynamic reservoir compartmentalization and well connectivity.