Identification of a prospect is normally done based on seismic interpretation and geological understanding of the area. However, due to the inherent uncertainties of the data we still observe in many cases that all key petroleum system elements are present, but still the drilled prospect is dry. Such failures are mostly attributed to a lack of understanding of seal capacity, reservoir heterogeneity, source rock presence and maturation, hydrocarbon migration, and relative timing of these processes. The workflow described in this paper aims to improve discovery success rates by deploying a more rigorous and structured approach. It is guided by the play-based exploration risk assessment process. The starting point is always that the process is guided by the the basic understanding of a mature kitchen should always be based on a regional scale petroleum systems model. However, while evaluating prospects, the migration and entrapment component of a prospect should always be investigated by means of a locally refined grid-based petroleum system model. The uniquepart of this approach is the construction of a high-resolution static model covering the prospects, which is built by using available well data, seismo-geological trends and attributes to capture reservoir potential. Additional inputs such as fault seal analysis also helps to understand prospect scale migration and associated geological risks. In the regional play and local prospect-scale petroleum system models, geological and geophysical inputs are utilized to create the uncertainty distribution for each input parameter which is required for assessing the success case volume of identified prospects. The evaluated risk is combined with the volumetric uncertainty in a probabilistic way to derive the risked volumetrics. It is further translated into an economic evaluation of the prospect by integrating inputs like estimated production profiles, appropriate fiscal models, HC price decks, etc. This enables the economic viability of the prospects to be assessed, resulting in a portfolio with proper ranking to build a decision-tree leading to execution and operations in ensuing drilling campaigns.
This paper considers Bayesian methods to discriminate between models depending on posterior model probability. When applying ensemble-based methods for model updating or history matching, the uncertainties in the parameters are typically assumed to be univariate Gaussian random fields. In reality, however, there often might be several alternative scenarios that are possible a priori. We take that into account by applying the concepts of model likelihood and model probability and suggest a method that uses importance sampling to estimate these quantities from the prior and posterior ensembles. In particular, we focus on the problem of conditioning a dynamic reservoir-simulation model to frequent 4D-seismic data (e.g., permanent-reservoir-monitoring data) by tuning the top reservoir surface given several alternative prior interpretations with uncertainty. However, the methodology can easily be applied to similar problems, such as fault location and reservoir compartmentalization. Although the estimated posterior model probabilities will be uncertain, the ranking of models according to estimated probabilities appears to be quite robust.
Hadavand, Mostafa (University of Alberta) | Carmichael, Paul (ConocoPhillips Canada) | Dalir, Ali (ConocoPhillips Canada) | Rodriguez, Maximo (ConocoPhillips Canada) | Silva, Diogo F. S. (University of Alberta) | Deutsch, Clayton Vernon (University of Alberta)
Mostafa Hadavand, University of Alberta; Paul Carmichael, Ali Dalir, and Maximo Rodriguez, ConocoPhillips Canada; and Diogo F. S. Silva and Clayton V. Deutsch, University of Alberta Summary 4D seismic is one of the main sources of dynamic data for heavy-oil-reservoir monitoring and management. Thus, the large-scale nature of fluid flow within the reservoir can be evaluated through information provided by 4D-seismic data. Such information may be described as anomalies in fluid flow that can be inferred from the unusual patterns in variations of a seismic attribute. During steam-assisted gravity drainage (SAGD), the steam-chamber propagation is fairly clear from 4D-seismic data mainly because of changes in reservoir conditions caused by steam injection and bitumen production. Anomalies in the propagation of the steam chamber reflect the quality of fluid flow within the reservoir. A practical methodology is implemented for integration of 4D seismic into SAGD reservoir characterization for the Surmont project. Introduction One of the main objectives in petroleum-reservoir modeling is to predict the future performance of the reservoir under a recovery process. It is not possible to establish the true spatial distribution of reservoir properties using limited data. Thus, the modeling process is ill-posed and subject to uncertainty (Pyrcz and Deutsch 2014). Geostatistical simulation provides a framework to quantify geological uncertainty that is represented by multiple equally probable realizations of the reservoir model. The uncertainty can be reduced by integration of all available sources of data, including static and dynamic (time-variant) data. However, each source of data provides information at different scales and levels of precision. Although there are well-established geostatistical techniques to generate stochastic realizations of the reservoir conditioned to static data, such as local measurements from wells and 2D/3D-seismic data, effective integration of dynamic data remains a major challenge. Time-lapse seismic, or 4D seismic, is one of the main dynamic sources of data for heavy-oil-reservoir monitoring and management. It contains valuable information regarding fluid movement, temperature, pressure buildup, and quality of fluid flow within the reservoir during a recovery process (Lumley and Behrens 1998; Gosselin et al. 2001). For SAGD, the evolution of the steam chamber over time is fairly clear in 4D-seismic images.
Azevedo, Leonardo (Cerena/Decivil, Instituto Superior Técnico) | Demyanov, Vasily (Institute of Petroleum Engineering, Heriot-Watt University) | Lopes, Diogo (Cerena/Decivil, Instituto Superior Técnico) | Soares, Amílcar (Cerena/Decivil, Instituto Superior Técnico) | Guerreiro, Luis (Partex Oil & Gas)
Geostatistical seismic inversion uses stochastic sequential simulation and co-simulation as the perturbation techniques to generate and perturb elastic models. These inversion methods allow retrieve high-resolution inverse models and assess the spatial uncertainty of the inverted properties. However, they assume a given number of a priori parametrization often considered known and certain, which is exactly reproduce in the final inverted models. This is the case of the top and base of main seismic units to which regional variogram models and histrograms are assigned. Nevertheless, the amount of existing well-log data (i.e., direct measurements) of the property to be inverted if often not enough to model variograms and its histograms are biased towards the more sand-prone facies. This work shows a consistent stochastic framework that allows to quantify uncertainties on these parameters which are associated with large-scale geological features. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions inferred from geological knowledge are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure, we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone. We couple stochastic adaptive sampling (i.e., particle swarm optimization) with global stochastic inversion to infer three-dimensional acoustic impedance from existing seismic reflection data. Key uncertain geological parameters are first identified, and reliable a priori distributions of potential values are assigned to each parameter. The type and shape of each distribution reflects the level of knowledge about this parameter. Then, particle swarm optimization is integrated as part of an iterative geostatistical seismic inversion methodology and these parameters are optimized along with the spatial distribution of acoustic impedance. At the end of the iterative procedure we retrieve the best-fit inverse model of acoustic impedance along with the most probable value for the location of top and base of each seismic unit, the most likely histogram and variogram model per zone.
In this work, we focus on a Bayesian inversion method for the estimation of reservoir properties from seismic data and we study how the inversion parameters, such as rock-physics and geostatistical parameters, can affect the inversion results in terms of reservoir performance quantities (pore volume and connectivity). We apply a Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of facies, acoustic impedance and porosity. The method is based on a Gibbs algorithm integrated with geostatistical methods that sample spatially correlated subsurface models from the posterior distribution. With the ensemble of multiples scenarios of the subsurface conditioned to the experimental data, we can evaluate two quantities that impact the production of the reservoir: the reservoir connectivity and the connected pore volume. For each set of parameters, the inversion method yields different results. Hence, we perform a sensitivity analysis for the main parameters of the inversion method, in order to understand how the subsurface model may be influenced by erroneous assumptions and parameter settings.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
He, Dongyang (China University of Petroleum-East China) | Yin, Xingyao (China University of Petroleum-East China) | Zong, Zhaoyun (China University of Petroleum-East China) | Li, Kun (China University of Petroleum-East China)
Summary Gaussian mixture model can be used to describe the multimodal behaviour of reservoir properties due to their variations within different discrete variables, such as facies. The weights of the Gaussian components represents the probabilities of the discrete variables. However, Bayesian linear inversion based on Gaussian mixture may misclassify discrete variables at some points, which may lead to a bad inversion result. In this study, we consider the spatial variability of discrete variables and combine Gaussian mixture model with the Sequential indicator simulation to determine the weight of each discrete variable in Sequential Bayesian linear inversion problems. We then can obtain the analytical solution of the Bayesian linear inverse problem and simultaneously classify the discrete variables.
There is a critical and growing need for information about subsurface geological properties and processes over sufficiently large areas that can inform key scientific and societal studies. Airborne geophysical methods fill a unique role in Earth observation because of their ability to detect deep subsurface properties at regional scales and with high spatial resolution that cannot be achieved with groundbased measurements. Airborne electromagnetics, or AEM, is one technique that is rapidly emerging as a foundational tool for geological mapping, with widespread application to studies of water and mineral resources, geologic hazards, infrastructure, the cryosphere, and the environment. Applications of AEM are growing worldwide, with rapid developments in instrumentation and data analysis software. In this study, we summarize several recent hydrogeophysical applications of AEM, including examples drawn from a recent survey in the Mississippi Alluvial Plain (MAP). In addition, we discuss developments in computational methods for geophysical and geological model structural uncertainty quantification using AEM data, and how these results are used in a sequential hydrogeophysical approach to characterize hydrologic parameters and prediction uncertainty.
Presentation Date: Wednesday, October 17, 2018
Start Time: 8:30:00 AM
Location: 213B (Anaheim Convention Center)
Presentation Type: Oral
Matthew, Free (Arup) | Esad, Porovic (Arup) | Jason, Manning (Arup) | Yannis, Fourniadis (Arup) | Richard, Lagesse (Arup) | Charlene, Ting (Arup) | Grace, Campbell (Arup) | Areti, Koskosidi (Arup) | Andrew, Farrant (BGS) | Ricky, Terrington (BGS) | Gareth, Carter (BGS) | Tarek, Omar (ADMA-OPCO)
This paper presents a new purpose-built digital interface for obtaining location-specific geological and geotechnical ground conditions for four oil and natural gas fields offshore of Abu Dhabi in the UAE. The geological model was developed using the software package GSI3D which was applied to an offshore study area for the first time. Statistically derived geotechnical parameters were used to apply a probabilistic approach for the design basis of geotechnical elements for offshore structures. In addition, geostatistical methods were applied in the treatment of geological uncertainty in the model. The model also includes a detailed review of local and regional natural hazards, including seismic, tsunami and submarine geohazards, with the potential to affect existing and proposed offshore infrastructure.
The tool comprises a fully interactive 3D geological and geotechnical ground model for each oil and gas field based on a geodatabase containing nearly 60 years of ground investigation data. The interface is operated through ESRI ArcMap but the geodatabase can be integrated into any online or offline GIS- based platform. Application of the tool enables effective decision making on key oil and gas development issues related to the siting of new exploration and development platforms and related infrastructure. The costs associated with offshore ground investigations are significant and mobilisation of works are heavily constrained by access, health, safety and environmental requirements. This digital tool will allow these works to be optimised at the advanced stages of planning, saving on time, cost and significantly reducing health, safety and environmental risks.
Pandey, Ajeet Kumar (Geodata Processing and Interpretation Centre, Oil and Natural Gas Corporation Limited) | Kumar, Vishwa (Geodata Processing and Interpretation Centre, Oil and Natural Gas Corporation Limited) | Bharsakle, Anuradha (Geodata Processing and Interpretation Centre, Oil and Natural Gas Corporation Limited) | Vasudevan, K. (Geodata Processing and Interpretation Centre, Oil and Natural Gas Corporation Limited) | Singh, Dhruvendra (Geodata Processing and Interpretation Centre, Oil and Natural Gas Corporation Limited)
Quantification of uncertainty in input parameters to build a robust 3D geological model is an integral and perhaps the most crucial requirement in high-risk exploration areas. This demands more innovative and effective management of uncertainties for optimizing reserve portfolios and better formulation of exploration and exploitation strategies for oil and gas fields. The present area of study pertains to Mumbai/Western Offshore Basin of India. The reservoirs of the study area are challenging due to their high spatio-temporal heterogeneity and discrete fluid distribution. Wells drilled during the field development plan are devoid of hydrocarbon from the lower zone of the reservoir (Middle Eocene/lower Bassein pay) while upper zone (Late Eocene/upper Bassein pay) produced significant amount of gas despite same lithological composition and structural setup, which reduced the utility of pressure-performance based or conventional modeling approach as it couldn't explain the complex geological set up of the deposition.
In this background, a thorough evaluation of critical aspect of most complex and anisotropic carbonate reservoir of Bassein Formation of Middle to Late Eocene age has been taken up to delineate the trends of favorable locales in the area. Inputs from micro-facies analysis, fluid transmissibility of Formations and diagenetic imprint analysis were considered to start the present study. An integrated methodology was designed incorporating seismic, well/logs, core samples, sedimentological, bio-stratigraphic & reservoir data to estimate petrophysical properties and necessary modifications in conventional approach were introduced for capturing the reservoir heterogeneity and stochasticity. Hi-frequency digenetic cycle mapping at log scale and pre-stack inversion results (P & S impedance, Vp/Vs ratio) were incorporated to build a robust geo cellular model and characterize the reservoir.
Uncertainty analysis presented in this study is mainly focused on structural and petrophysical parameters. The effect of each parameter/factor and their interaction effect (response) with other parameters are analyzed through Optimization Algorithms, to quantify the uncertainties and its impact on reservoir characterization. Sensitivity analysis indicated that Oil Initially In Place (OIIP) exhibits significant sensitivity to effective porosity and water saturation. Therefore, distribution pattern of these uncertainty parameters are derived from Probability Density Function (PDF) and used to restrict the variability of the volumetric estimates to retain the P10/P90 ratios within the acceptable ranges.
Quantification of structural parameter was performed using non-linear multiple regression technique, constrained by statistically Maximum Allowable Error (+Standard Deviation).
Present analysis enabled us to reduce the uncertainty associated with various reservoir characterization elements. Further, it enhanced robustness of velocity modeling, petrophysical and lithological interpretation through determination of uncertainties with high degree of accuracy and provided their role in estimation of final hydrocarbon-in-place volumes. The parameterization of the uncertainties deliberated could be used as a template in other fields sharing similar structural and depositional characteristics to mitigate the risks associated with Field Development Plan.
The complex nature of deep water sediments requires the use of a full field 3D static model to enable better understanding of the reservoir characteristics of the field. This study focuses on the 3D static modelling of the 458 reservoir in Botti field to facilitate field development. The Botti field is a partially appraised field located about 20km offshore Nigeria. A total of four wells have been drilled in the field and only two wells encountered the target reservoir. The depositional environment is mainly deep water slope channel sands with some submarine fans. The morphological uncertainties relating to the slope channel sands deposits, requires a detailed 3D static model which defines reservoir characteristics such as channel orientation, continuity and connectivity.