|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Estimating resource and reserves crosses the disciplines between geoscientists and petroleum engineers. While the geoscientist may well have primary responsibility, the engineer must carry the resource and reserve models forward for planning and economics. Volumetric estimates of reserves are among the most common examples of Monte Carlo simulation. Consider the following typical volumetric formula to calculate the gas in place, G, in standard cubic feet. In this formula, there is one component that identifies the prospect, A, while the other factors essentially modify this component.
Ezabadi, Mehdi Ghane (PETRONAS Carigali Sdn. Bhd.) | Ataei, Abdolrahim (PETRONAS Carigali Sdn. Bhd.) | Liang, Tan Kok (PETRONAS Carigali Sdn. Bhd.) | Motaei, Eghbal (PETRONAS Carigali Sdn. Bhd.) | Othman, Tg Rasidi (PETRONAS Carigali Sdn. Bhd.)
Abstract Production Data Analysis (PDA) has been widely accepted as a valuable analytical tool for well performance evaluation, production forecasting and reservoir characterization. It is fast, practical, and inexpensiveand it can answer many questions about the connected volume to the well, flow regime, average permeability and skin, as well as any boundary within the radius of investigation of the well. It becomes even more important in the case of complex systems such as finely laminated sand reservoirs, or highly heterogeneous multi-stacked reservoirs where sometimes numerical simulation model miscarries in predicting the reservoir performance. Analytical approaches for PDA are variants and require different levels of details in the input. Each is established based on certain assumptions and concepts, and comes with specific limitations. Despite overlap amongst the various methods, each has an advantage in particular application over the others. Therefore, one must be vigilant to use each method for the right purposes in addition to confirm the results and unveil possible uncertainties through using several different methods. This paper integrates basic production and reservoir data through different platforms and methods. Diagnostic plots, General Material Balance (GMB), Pressure Transient Analysis (PTA), deconvolution, nodal analysis, Rate Transient Analysis (RTA), and Flowing Material Balance (FMB) are extensively used to explain the reservoir behavior through PDA. It validates RTA and FMB as an approach for reservoir characterization and reserve estimation without the need to shut-in the well, and defer the production. The benefit of continuously monitoring Flowing Bottom Hole Pressure (FBHP) using Permanent Downhole Gauge (PDG) and applying deconvolution to detect well interference and reservoir boundaries is also discussed. We have also looked at the limitation and advantage of each method and how the integration of those can provide a full picture and enhance the results. We have studied several gas fields. The results of analysis provided an accurate perception and understanding of reservoir behavior and characteristics, well interaction and interference, potential for infill wells, production issues and well constraints, estimation of the connected volume, and eventually led to generation of a reliable analytical reservoir model for the production forecast. The estimated connected volume was tested and proved to be reliable based on infill drilling. The workflow focuses on examining the data quality, confirming the validity of work, and achieving the maximum possible insight through integration of different analytical methods. An integrated workflow is introduced for PDAand successfully applied on different cases of highly heterogeneous conventional gas reservoirs with huge complexities. The paper demonstrates one of the case study as example. The proposed workflow shows to be very powerful particularly when large volume of data from pressure downhole gauges (PDG) is available. It saves significant time for the study team in determining the potential value of a project.
Abstract To perform an optimization study for a green field (newly discovered field), one must collect the information from different parts of the field and integrate these data as accurately as possible in order to construct the reservoir image. Once the image, or alternate images, are constructed, reservoir simulation allows prediction of dynamic performance of the reservoir. As field development progresses, more information becomes available, enabling us to continually update and, if needed, correct the reservoir description. The simulator can then be used to perform a variety of exercises or scenarios, with the goal of optimizing field development and operation strategies. We are often confronted with important questions related to the most efficient well spacing and location, the optimum number of wells needed, the size of the production facility needed, the optimum production strategies, the location of the external boundaries, the intrinsic reservoir properties, the predominant recovery mechanism, the best time and location to employ infill drilling and the best time and type of the improved recovery technique we should implement. These are some of the critical questions we may need to answer. A reservoir simulation study is the only practical means by which we can design and run tests to address these questions in sufficient detail. From this perspective, reservoir simulation is a powerful screening tool. The magnitude, time and complexity of a reservoir simulation problem depends in part on the available computational environment. For instance, simple material balance calculations are now routinely performed on desktop personal computers, while running a field-scale three-dimensional simulator may call for the use of a supercomputer and may take many days to finish. We must also take into account the storage requirements and limitations, CPU time demand and the general architecture of the machine. The problem arises when there is a large amount of data available with a study objective that requires running several scenarios incorporating millions of grid cells. This will limit the applicability of reservoir simulation as it will be computationally very inefficient. For example, determining the optimum well locations in a field that will result in the most efficient production rate scenario requires a large number of simulation runs which can make it very inefficient. This is because one will have to consider multiple well scenarios in multiple realizations. The main purpose of this paper is to use a novel methodology known as the Fast Marching Method (FMM) to find the optimum well locations in a green oil field that will result in the most efficient production rate scenario. FMM tracks a pressure front from a well and can approximately determine the drainage radius. For single phase fluid, we can determine the rate profile for a well. By quickly generating rate profiles for multiple scenarios, we can rank multiple realizations with multiple well scenarios in matter of minutes.
Abstract This project focuses on building a reservoir sub-sea network model for a condensate field in the gulf of Guinea, the Duke Field. It integrates the five developed Duke reservoirs, development wells and subsea network using the Petroleum Experts' Integrated Production Model suite of software, (IPM) which is widely used in the E&P industry especially for integrated forecasting, surveillance and production system optimization that require integration of surface and subsurface models. Following the acquisition and quality control of data from other teams working on the Duke Field, a network model which integrates the five Duke reservoirs, their associated wells and subsea network up to the production separator was built. The model was initialized and used to predict full field performance under different scenarios. Finally, a water injection allocation sensitivity study was performed and the results were analyzed both technically and economically. From the technical point of view, the option to reallocate 10 kbwpd from reservoir U to reservoir P-upper North and another 10 kbwpd from Reservoir ST to reservoir Q-Lower brought about the optimum recovery. This was also supported by a simple economic analysis. It was then recommended that additional water injectors be drilled in P-Upper North and Q-Lower to unlock an additional 8.4 MMSTB of reserves resulting from higher sweep efficiencies and better pressure maintenance.
Abstract The building of 3D models from seismic, geological, petrophysical, well, drilling, reservoir and production engineering data which are used in the study of optimal recovery of hydrocarbons from petroleum reservoirs has become commonplace. During the static modeling phase of the workflow, there is the need for the application of limits on the petrophysical properties to differentiate reservoir from non-reservoir rock. These limits are called cutoffs: limiting values of petrophysical properties in the static models. In this case study, multiple regression analysis was used to derive the best relationship of the form k = f(Ø, Vsh, Sw) which was then used to estimate optimum petrophysical cutoffs for the reservoir. Four static models of the reservoir were then built: the first model with only a Vsh cutoff of 0.42 to delineate reservoir rock from the non-reservoir rock. The other 3 utilized various Ø, Vsh and Sw cutoffs in their building. Four dynamic models of the reservoir were then built from the static models and were history matched to observed production and pressure data. Performance prediction was then carried out using the different history matched models. Finally, using the results of the forecast and other data, an economic analysis was carried out to determine the economic implications of the different cutoffs on the results of the studies. The results showed that the values of the Ø cutoff affected the values of the corresponding Vsh and Sw cutoffs as well as those of the HCIIPs, with the general trend being that a reduction in the Ø cutoff increased the values of the Vsh and Sw cutoffs. The economic analysis showed that petrophysical cutoffs used in the development of the static (and the dynamic) models sure had some impact on the results of the analysis.
Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition held in San Antonio, Texas, USA, 8-10 October 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Throughout the upstream petroleum industry, reservoir simulation models have become the standard tool used to make a myriad of decisions regardingg the development and operation of oil andd gas assets including decisions regarding investments and reserves estimates. Given the variety of decisions that rely on this technology, petroleum engineers are frequently required to opine on how well an unfamiliar model might fit a particular purpose, and how accurate it might bee for that purpose. Generally speaking, engineers must go through some sort of review of the model before any opinion of fit and accuracy can be developed. The authors have written or contributed to several papers that began to address the process for performing such simulation model reviews for various purposes (see Palke and Rietz 2001, and Rietz and Usmani 2005). This paper attempts to advance this work by presenting a systematic and quantitative methodology for performing these reviews, as well as discussing why such reviews are necessary and some of the potential pitfalls encounteredd while reviewing simulation models.
Abstract Equity redetermination is most commonly encountered where a straddling field is developed as a discrete entity through a process of unitization. It is enacted through evaluation procedures that prescribe the technical methodology for requantifying different license shares, or tract participations, in a field unit as more data become available. The formulation of these procedures usually takes place at unitization, it is based on appraisal data, and therefore it is guided by simplified perceptions of reservoir character. For this reason, many such technical procedures have been found to be lacking when they are applied later at the equity redetermination stage. These shortcomings can take the form of ambiguous wording, misleading definitions, technically-inappropriate specifications, contradictory prescription, or simply a lack of sufficient detail to render the intended process meaningful. They have impeded the determination of revised tract participations by triggering inter-license disagreements that might otherwise have been avoided. With the objective of reducing this unhelpful impact, experience of redetermination situations is used to illustrate the nature and consequences of poorly constructed procedures for the recomputation of tract participations. The analysis is then flipped to generate a framework of key elements of technical procedures together with indications of how they are best implemented. These matters form the basis for a high-level set of protocols for a more efficient and effective redetermination of equity that would avoid the previously encountered shortcomings. The protocols encompass the proper incorporation of data character, a sound technical basis for redetermination, a balance between under- and over-prescription, an auditable deterministic ethos, and adherence to good international petroleum practice. They constitute recommendations for a better approach to the compilation of fit-for-purpose evaluation procedures within those unitization agreements that make provision for a future redetermination of equity. The recommendations are equally applicable to domestic and international unitizations. The principal benefit lies in an enhanced efficiency of the equity-redetermination process, which feeds through to a greater collective asset value.
Abstract Development optimization of new fields can be improved with integrated quantitative models that account for the technical and economical aspects of hydrocarbon recovery. A challenge in implementation is to understand the potential impact of uncertainty on optimal decision-making. To mitigate the risks and seize the opportunities arising from the uncertainty, the models used in the decision-making process should include a robust capability for stochastic optimization. This paper presents a case study in development optimization of a two-compartment offshore gas field. The analysis focuses on the optimization of facility size, well counts, compression power and production policy. A stochastic programming model is developed to investigate the impact of uncertainties in original gas in place and inter-compartment transmissibility. Reservoir tank equations are used to model pressure and production responses. The reservoir and well equations are coupled with economic and surface facility models. Results of two solution methods, optimization with Monte Carlo sampling and stochastic programming, are analyzed and compared. The models are then used in a value of information (VOI) analysis. The current work is part of an emerging effort in industry to introduce fast and efficient methods for optimizing field development under uncertainty. Computational efficiency is a significant advantage of the proposed approach because it eliminates most constraints on the scope of the uncertainty analysis. The intended applications of this approach are project screening, scenario and uncertainty analyses, including VOI analysis.
SPE, _ (Society of Petroleum Engineers) | AAPG, _ (American Association of Petroleum Geologists) | WPC, _ (World Petroleum Council) | SPEE, _ (Society of Petroleum Evaluation Engineers) | SEG, _ (Society of Exploration Geophysicists)
Society of Petroleum Engineers (SPE)
American Association of Petroleum Geologists (AAPG)
World Petroleum Council (WPC)
Society of Petroleum Evaluation Engineers (SPEE)
Society of Exploration Geophysicists (SEG)
1.1 Rationale for New Applications Guidelines
SPE has been at the forefront of leadership in developing common standards for petroleum resource definitions. There has been recognition in the oil and gas and mineral extractive industries for some time that a set of unified common standard definitions is required that can be applied consistently by international financial, regulatory, and reporting entities. An agreed set of definitions would benefit all stakeholders and provide increased
A milestone in standardization was achieved in 1997 when SPE and the World Petroleum Council (WPC) jointly approved the “Petroleum Reserves Definitions.” Since then, SPE has been continuously engaged in keeping the definitions updated. The definitions were updated in 2000 and approved by SPE, WPC, and the American Association of Petroleum Geologists (AAPG) as the “Petroleum Resources Classification System and Definitions.” These were updated further in 2007 and approved by SPE, WPC, AAPG, and the Society of Petroleum Evaluation Engineers (SPEE). This culminated in the publication of the current “Petroleum Resources Management System,” globally known as PRMS. PRMS has been acknowledged as the oil and gas industry standard for reference and has been used by the US Securities and Exchange Commission (SEC) as a guide for their updated rules, “Modernization of Oil and Gas Reporting,” published 31 December 2008.
SPE recognized that new applications guidelines were required for the PRMS that would supersede the 2001 Guidelines for the Evaluation of Petroleum Reserves and Resources. The original guidelines document was the starting point for this work, and has been updated significantly with addition of the following new chapters:
Estimation of Petroleum Resources Using Deterministic Procedures (Chap. 4)
Unconventional Resources (Chap. 8)
In addition, other chapters have been updated to reflect current technology and enhanced with examples. The document has been considerably expanded to provide a useful handbook for many reserves applications. The intent of these guidelines is not to provide a comprehensive document that covers all aspects of reserves calculations because that would not be possible in a short, precise update of the 2001 document. However, these expanded new guidelines serve as a very useful reference for petroleum professionals.
Chap. 2 provides specific details of PRMS, focusing on the updated information. SEG Oil and Gas Reserves Committee has taken an active role in the preparation of Chap. 3, which addresses geoscience issues during evaluation of resource volumes. The chapter has been specifically updated with recent technological advances. Chap. 4 covers deterministic estimation methodologies in considerable detail and can be considered as a stand-alone document for deterministic reserves calculations. Chap. 5 covers approaches used in probabilistic estimation procedures and has been completely revised. Aggregation of petroleum resources within an individual project and across several projects is covered in Chap. 6, which has also been updated. Chap. 7 covers commercial evaluations.
Chap. 8 addresses some special problems associated with unconventional reservoirs, which have become an industry focus in recent years. The topics covered in this chapter are a work in progress, and only a high-level overview could be given. However, detailed sections on coalbed methane and shale gas are included. The intent is to expand this chapter and add details on heavy oil, bitumen, tight gas, gas hydrates as well as coalbed methane and shale as the best practices evolve.
Production measurement and operations issues are covered in Chapter 9 while Chapter 10 contains details of resources entitlement and ownership considerations. The intent here is not to provide a comprehensive list of all scenarios but furnish sufficient details to provide guidance on how to apply the PRMS.A list of Reference Terms used in resources evaluations is included at the end of the guidelines. The list does not replace the PRMS Glossary, but is intended to indicate the chapters and sections where the terms are used in these Guidelines.