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Abstract Quantifying, ranking and weighting of reservoir uncertainties are a key element in evaluating the development plan of a green field. Due to the limited amount of data and its accuracy, it is of prime interest to assess the risk related to a development plan in terms of potential recovery. Once quantified, mitigation plans or probabilistic optimization can be derived in order to minimize the risk and maximize the recovery. The risk assessment of the development plan in this study was applied to a new offshore field of Abu Dhabi. The stochastic uncertainty analysis consisted in defining the main variables contributing to the risk on ultimate recovery and ranking them using uncertainty and risk analysis tool. The results highlighted a strong impact of fault sealing capability and residual oil among other parameters together with a dependency of the parameters’ impact to time. Given these uncertainties it could then be identified that the current FDP was most likely overestimating the actual P50 and that remedial actions shall be put in place to acquire more confidence in the data. Eventually an optimization under uncertainty provided a better insight on the optimal field rate during the production plateau and required shut-in water cut for producing wells in order to maximize the field recovery.
- North America > United States (0.93)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.72)
Summary This paper details an alternative method to the commonly used geostatistical simulation approaches for uncertainty analysis, which are generally time consuming and do not give access to uncertainties associated to calibration sets. The proposed approach uses a deterministic route to evaluate inversion uncertainties and then propagates them into the seismic characterization workflow in order to predict jointly properties and associated uncertainties. In practice it associates a Bayesian inversion method to estimate elastic parameters and a "bootstrap" method for property estimation and uncertainty assessment. Such approach appears to be particularly well adapted for the case study presented, as the main source of uncertainty is related to the calibration set: limited number of calibration samples and uncertain seismic attributes (impedance from inversion). The method allows assessing the uncertainties while turning the attributes into reservoir properties, as well as propagating the uncertainties attached to the attributes in the interpretation process.
- Geophysics > Seismic Surveying > Seismic Interpretation (0.56)
- Geophysics > Seismic Surveying > Seismic Modeling (0.48)
Summary Prudent decision making in subsurface assets requires reservoir uncertainty quantification. In a typical uncertainty-quantification study, reservoir models must be updated using the observed response from the reservoir by a process known as history matching. This involves solving an inverse problem, finding reservoir models that produce, under simulation, a similar response to that of the real reservoir. However, this requires multiple expensive multiphase-flow simulations. Thus, uncertainty-quantification studies employ optimization techniques to find acceptable models to be used in prediction. Different optimization algorithms and search strategies are presented in the literature, but they are generally unsatisfactory because of slow convergence to the optimal regions of the global search space, and, more importantly, failure in finding multiple acceptable reservoir models. In this context, a new approach is offered by estimation-of-distribution algorithms (EDAs). EDAs are population-based algorithms that use models to estimate the probability distribution of promising solutions and then generate new candidate solutions. This paper explores the application of EDAs, including univariate and multivariate models. We discuss two histogram-based univariate models and one multivariate model, the Bayesian optimization algorithm (BOA), which employs Bayesian networks for modeling. By considering possible interactions between variables and exploiting explicitly stored knowledge of such interactions, EDAs can accelerate the search process while preserving search diversity. Unlike most existing approaches applied to uncertainty quantification, the Bayesian network allows the BOA to build solutions using flexible rules learned from the models obtained, rather than fixed rules, leading to better solutions and improved convergence. The BOA is naturally suited to finding good solutions in complex high-dimensional spaces, such as those typical in reservoir-uncertainty quantification. We demonstrate the effectiveness of EDA by applying the well-known synthetic PUNQ-S3 case with multiple wells. This allows us to verify the methodology in a well-controlled case. Results show better estimation of uncertainty when compared with some other traditional population-based algorithms.
- Asia (0.93)
- North America > United States > Texas (0.68)
- Europe > United Kingdom (0.68)
- North America > United States > California (0.46)
- Overview > Innovation (0.48)
- Research Report > New Finding (0.48)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (23 more...)
- Reservoir Description and Dynamics > Reservoir Simulation > History matching (1.00)
- Reservoir Description and Dynamics > Reservoir Simulation > Evaluation of uncertainties (1.00)
- Management > Risk Management and Decision-Making > Risk, uncertainty, and risk assessment (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Search (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Information Retrieval (1.00)
- (2 more...)
The authors present a vulnerability and risk analysis of tsunami hazard for the city of Rhodes. The tsunami hazard is assessed through computed values of the maximum inundation and maximum flow depth derived from a probabilistic scenario for a 1000-year time window, which incorporates hundreds of numerical simulations with MOST code. The data needed to identify tsunami vulnerable areas are gathered combining remote sensing techniques and GIS technology with surveyed observations and estimates of population data. Tsunami risk zones are defined on the basis of both estimated maximum inundation and maximum flow depth data and results are presented using GIS. INTRODUCTIONM Major tsunamis are rare events in the Mediterranean, where they are believed to occur a few times per century. Nevertheless, as historical records indicate, the island of Rhodes in SE Aegean has experienced severe earthquakes, such as the earthquakes of 1303, 1481 and 1741 AD, which are related, with variable degree of confidence, to the occurrence of tsunamis, see e.g. Ambraseys (1962), Papadopoulos and Chalkis (1984), Ambraseys and Synolakis (2010). Nowadays, the potential impact due to an extreme event is likely to be much greater since urban development is rapidly increasing in coastal areas. Until 2004, there were limited studies on tsunami hazard and risk assessment, for specific locales in the Mediterranean Sea and particularly for Greece. Indicatively, Papadopoulos and Dermentzopoulos (1998) performed a qualitative tsunami risk pilot management study for Heraklion, Crete, their results being based upon the analysis of a hypothetical tsunami of a particular magnitude with no numerical modelling. Papathoma et al. (2003) and Papathoma and Dominey-Howes (2003) proposed a vulnerability approach incorporating various vulnerability factors in order to assign the so-called Relative Vulnerability Index to every building located inside the inundation zone. The latter was not derived from simulations but was rather defined as the area between the coastline and the 5m elevation contour.
- Europe (1.00)
- North America > United States > California (0.47)
- Transportation > Air (0.46)
- Energy > Oil & Gas > Upstream (0.34)
- Health, Safety, Environment & Sustainability (1.00)
- Data Science & Engineering Analytics > Information Management and Systems (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (0.93)
- Management > Risk Management and Decision-Making > Risk, uncertainty, and risk assessment (0.69)
Abstract Given the high degree of uncertainty in the oil industry present production development projects, the use of probabilistic models is of considerable interest as a means to support decision making. Besides that, there is a constant pursuit to optimize oil drainage, to maximize reserves as well as the financial outcome of the project. A robust study, characterizing the uncertainty of the various critical parameters, such as number of wells, maximum water injection rates, relative permeabilities and rock and PVT data, is of paramount importance to assure that the impact of all uncertain parameters have been accounted for. The present risk curve construction methodology takes into consideration uncertainties in the geological model and in the dynamic properties in an integrated manner. Six geologic scenarios with distinct permo-porous distributions were generated. A study was conducted to obtain PVT correlations of the produced fluids. A sensitivity analysis was performed to eliminate non-significant parameters. The relevant ones were well adjusted to the production data in all geologic scenarios. Optimum exploitation configurations were obtained for each model, using the net present value (NPV) as an objective function. Each configuration was applied to all scenarios and the estimated monetary values (EMV) of each configuration were calculated. The maximum EMV was used as optimum criteria. The final product obtained was a development strategy risk curve, showing the viability of the proposed methodology.
- South America > Brazil > Sergipe (0.28)
- North America > United States > Texas (0.28)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.55)
- South America > Brazil > Alagoas > South Atlantic Ocean > South Atlantic Ocean > Sergipe-Alagoas Basin (0.99)
- South America > Brazil > Alagoas > Sergipe > South Atlantic Ocean > Sergipe-Alagoas Basin (0.99)
Abstract While CO2 Capture and Sequestration (CCS) is considered a part of the solution to overcoming the ever increasing level of CO2 in the atmosphere, one must be sure that significant new hazards are not created by the CO2 injection process. The risks involved in different stages of a CO2 sequestration project are related to geological and operational uncertainties. This paper presents the application of a grid-based Surrogate Reservoir Model (SRM) to a real case CO2 sequestration project in which CO2 were injected into a depleted gas reservoir. An SRM is a customized model that accurately mimics reservoir simulation behavior by using Artificial Intelligence & Data Mining techniques. Initial steps for developing the SRM included constructing a reservoir simulation model with a commercial software, history matching the model with available field data and then running the model under different operational scenarios or/and different geological realizations. The process was followed by extracting some static and dynamic data from a handful of simulation runs to construct a spatio-temporal database that is representative of the process being modeled. Finally, the SRM was trained, calibrated, and validated. The most widely used Quantitative Risk Analysis (QRA) techniques, such as Monte Carlo simulation, require thousands of simulation runs to effectively perform the uncertainty analysis and subsequently risk assessment of a project. Performing a comprehensive risk analysis that requires several thousands of simulation runs becomes impractical when the time required for a single simulation run (especially in a geologically complex reservoir) exceeds only a few minutes. Making use of surrogate reservoir models (SRMs) can make this process practical since SRM runs can be performed in minutes. Using this Surrogate Reservoir Model enables us to predict the pressure and CO2 distribution throughout the reservoir with a reasonable accuracy in seconds. Consequently, application of SRM in analyzing the uncertainty associated with reservoir characteristics and operational constraints of the CO2 sequestration project is presented.
Application of Well-Based Surrogate Reservoir Models (SRMs) to Two Offshore Fields in Saudi Arabia, Case Study
Mohaghegh, Shahab D. (West Virginia University & Intelligent Solutions, Inc.) | Liu, Jim (Saudi Aramco) | Gaskari, Razi (Intelligent Solution Inc.) | Maysami, Mohammad (Intelligent Solution Inc.) | Olukoko, Olugbenga (Saudi Aramco)
Abstract Well-based Surrogate Reservoir Model (SRM) may be classified as a new technology for building proxy models that represent large, complex numerical reservoir simulation models. The well-based SRM has several advantages over traditional proxy models, such as response surfaces or reduced models. These advantages include (1) to develop an SRM one does not need to approximate the existing simulation model, (2) the number of simulation runs required for the development of an SRM is at least an order of magnitude less than traditional proxy models, and (3) above and beyond representing the pressure and production profiles at each well individually, SRM can replicate, with high accuracy, the pressure and saturation changes at each grid block. Well-based SRM is based on the pattern recognition capabilities of artificial intelligence and data mining (AI&DM) that is also referred to as predictive analytics. During the development process the SRM is trained to learn the principles of fluid flow through porous media as applied to the complexities of the reservoir being modeled. The numerical reservoir simulation model is used for two purposes: (1) to teach the SRM the physics of fluid flow through porous media as applied to the specific reservoir that is being modeled, and (2) to teach the SRM the complexities of the heterogeneous reservoir represented by the geological model and its impact on the fluid production and pressure changes in the reservoir. Application of well-based SRM to two offshore fields in Saudi Arabia is demonstrated. The simulation model of these fields includes millions of grid blocks and tens of producing and injection wells. There are four producing layers in these assets that are contributing to production. In this paper we provide the details that is involved in development of the SRM and show the result of matching the production from the all the wells. We also present the validation of the SRM through matching the results of blind simulation runs. The steps in the development of the SRM includes design of the required simulation runs (usually less than 20 simulation runs are sufficient), identifying the key performance indicators that control the pressure and production in the model, identification of input parameters for the SRM, training and calibration of the SRM and finally validation of the SRM using blind simulation runs.
- North America > United States (1.00)
- Africa (0.94)
- Asia > Middle East > Saudi Arabia (0.86)
ABSTRACT ABSTRACT Risk is defined as a product of the probability of a hazard causing an adverse event combined with the severity (or consequences) of that adverse event. Accordingly, pipeline risk management should integrate both the concepts of failure frequency and the potential consequences for each hazard scenario. Pipelines’ risk assessment is particularly challenging because pipelines cover extended geographic regions and there are numerous threats to pipeline integrity. Consequences are not always easy to evaluate depending on many parameters such as type of product being transported and terrain. Furthermore, physical models and statistical data cannot completely capture the complex time-based phenomena leading to failures. On the other hand, risk ranking or scoring methods often appear to be arbitrary in terms of how the final risk score is computed. To solve this problem, a pipeline risk assessment model has been created using a probabilistic graphical model called a Bayesian Network. The model calculates separately internal and external corrosion risks, manufacturing and construction risks, natural hazard risks, third party damage risks and maintenance/operation error risks. Moreover, these risks might lead to a “loss of containment” which has four different consequences: financial impact, environmental impact, effects on health and safety, and public outrage. The Bayesian network model is illustrated through a specific example involving internal corrosion. The geographically-based risk assessment allows the user to prioritize effectively pipeline inspections and repairs. INTRODUCTION Pipeline risk assessment can be a daunting task. A good pipeline risk assessment method should be able to characterize and quantify the risk associated to a pipeline. Therefore, scenarios leading to failure consequences, the probability of a failure (converse of reliability) and the outcome of a failure (consequence) have to be assessed in order to calculate pipeline’s the overall pipeline’s risk. Most pipeline risk assessment methods deal only with only one or two parts of the risk triplet.
- Europe (1.00)
- North America > United States > Texas > Harris County > Houston (0.16)
- Government > Regional Government > North America Government > United States Government (1.00)
- Energy > Oil & Gas > Upstream (1.00)
- Energy > Oil & Gas > Midstream (1.00)
- (2 more...)
- Management > Risk Management and Decision-Making > Risk, uncertainty, and risk assessment (1.00)
- Health, Safety, Environment & Sustainability (1.00)
- Facilities Design, Construction and Operation (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Application of Surrogate Reservoir Model (SRM) to an Onshore Green Field in Saudi Arabia; Case Study
Mohaghegh, Shahab D. (Intelligent Solutions, Inc. & West Virginia University) | Liu, Jim (Saudi Aramco) | Gaskari, Razi (Intelligent Solutions, Inc.) | Maysami, Mohammad (Intelligent Solutions, Inc.) | Olukoko, Olugbenga A. (Saudi Aramco)
Abstract Application of the Surrogate Reservoir Model (SRM) to an onshore green field in Saudi Arabia is the subject of this paper. SRM is a recently introduced technology that is used to tap into the unrealized potential of the reservoir simulation models. High computational cost and long processing time of reservoir simulation models limit our ability to perform comprehensive sensitivity analysis, quantify uncertainties and risks associated with the geologic and operational parameters or to evaluate a large set of scenarios for development of green fields. SRM accurately replicates the results of a numerical simulation model with very low computational cost and low turnaround period and allows for extended study of reservoir behavior and potentials. SRM represents the application of artificial intelligence and data mining to reservoir simulation and modeling. In this paper, development and the results of the SRM for an onshore green field in Saudi Arabia is presented. A reservoir simulation model has been developed for this green field using Saudi Aramco's in-house POWERS™ simulator. The geological model that serves as the foundation of the simulation model is developed using an analogy that incorporates limited measured data augmented with information from similar fields producing from the same formations. The reservoir simulation model consists of 1.4 million active grid blocks, including 40 vertical production wells and 22 vertical water injection wells. Steps involved in developing the SRM are identifying the number of runs that are required for the development of the SRM, making the runs, extracting static and dynamic data from the simulation runs to develop the necessary spatio-temporal dataset, identifying the key performance indicators (KPIs) that rank the influence of different reservoir characteristics on the oil and gas production in the field, training and matching the results of the simulation model, and finally validating the performance of the SRM using a blind simulation run. SRM for this reservoir is then used to perform sensitivity analysis as well as quantification of uncertainties associated with the geological model. These analyses that require thousands of simulation runs were performed using the SRM in minutes.
- Energy > Oil & Gas > Upstream (1.00)
- Government > Regional Government > Asia Government > Middle East Government > Saudi Arabia Government (0.55)
- Information Technology > Modeling & Simulation (1.00)
- Information Technology > Data Science > Data Mining (0.88)