Uranium Reserves Worldwide

1993 ◽  
Vol 11 (1) ◽  
pp. 32-41
Author(s):  
R. Bhar

The Uranium Institute has developed a set of definitions for classifying uranium resources and places the resources into three cost categories. The definitions are given in full, and are supplemented by tables giving the current statistics together with a production forecast to the year 2010. All the statistics are derived from current Uranium Institute reports.

Author(s):  
A. Chaterine

This study accommodates subsurface uncertainties analysis and quantifies the effects on surface production volume to propose the optimal future field development. The problem of well productivity is sometimes only viewed from the surface components themselves, where in fact the subsurface component often has a significant effect on these production figures. In order to track the relationship between surface and subsurface, a model that integrates both must be created. The methods covered integrated asset modeling, probability forecasting, uncertainty quantification, sensitivity analysis, and optimization forecast. Subsurface uncertainties examined were : reservoir closure, regional segmentation, fluid contact, and SCAL properties. As the Integrated Asset Modeling is successfully conducted and a matched model is obtained for the gas-producing carbonate reservoir, highlights of the method are the following: 1) Up to ± 75% uncertainty range of reservoir parameters yields various production forecasting scenario using BHP control with the best case obtained is 335 BSCF of gas production and 254.4 MSTB of oil production, 2) SCAL properties and pseudo-faults are the most sensitive subsurface uncertainty that gives major impact to the production scheme, 3) EOS modeling and rock compressibility modeling must be evaluated seriously as those contribute significantly to condensate production and the field’s revenue, and 4) a proposed optimum production scenario for future development of the field with 151.6 BSCF gas and 414.4 MSTB oil that yields a total NPV of 218.7 MMUSD. The approach and methods implemented has been proven to result in more accurate production forecast and reduce the project cost as the effect of uncertainty reduction.


Author(s):  
Mark Blaxill ◽  
Toby Rogers ◽  
Cynthia Nevison

AbstractThe cost of ASD in the U.S. is estimated using a forecast model that for the first time accounts for the true historical increase in ASD. Model inputs include ASD prevalence, census population projections, six cost categories, ten age brackets, inflation projections, and three future prevalence scenarios. Future ASD costs increase dramatically: total base-case costs of $223 (175–271) billion/year are estimated in 2020; $589 billion/year in 2030, $1.36 trillion/year in 2040, and $5.54 (4.29–6.78) trillion/year by 2060, with substantial potential savings through ASD prevention. Rising prevalence, the shift from child to adult-dominated costs, the transfer of costs from parents onto government, and the soaring total costs raise pressing policy questions and demand an urgent focus on prevention strategies.


2021 ◽  
Vol 4 (7) ◽  
pp. 80-90
Author(s):  
Bahromjon Rahimov ◽  
◽  
Mirzobobur Ibrokhimov

The establishment of market relations in agriculture requires the development of the system of material and technical resources on the basis of market principles. Weak financial situation of agricultural enterprises, weakening of economic relations with the manufacturer of equipment, transport costs, transit, high customs duties, devaluation of money, imbalances between prices for agricultural and industrial products and a number of other factors.Keywords:regions, efficiency, economy, agricultural production, forecast, production potential, intensification, resources, economic mechanism.


2021 ◽  
Author(s):  
Boxiao Li ◽  
Hemant Phale ◽  
Yanfen Zhang ◽  
Timothy Tokar ◽  
Xian-Huan Wen

Abstract Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.


2021 ◽  
Author(s):  
Iman Al Selaiti ◽  
Maged Mabrook ◽  
Mohammad Faizul Hoda ◽  
Luigi Saputelli ◽  
Hafez Hafez ◽  
...  

Abstract Production planning and performance management imply diverse challenges, mainly when dealing at corporate level in an integrated operating company. Production forecast considers technical capacities, available capacities, and operationally agreed target capacities. Such complex process may hinder taking advantage of market opportunities at the right time. Proactive scenario management and information visibility across the organization are key for success. This paper intends to share the lessons learned while rolling out a countrywide integrated capacity model solution supporting corporate production planning and performance management. The rollout processes aimed at digitizing the monthly and yearly production forecasting. In addition, these processes shall enable formulating proactive scenarios for avoiding shortfalls, maximizing gas throughput, production ramp up, and minimizing operating cost from existing capacity. Abu Dhabi's Integrated Capacity Model is an integrated production planning and optimization system relying on a large-scale subsurface-to-surface integrated asset model system; in this paper, we focus on the incremental progress of the challenges derived from the various rollout efforts. The rollout of such a complex solution relies on basic tenets for managing the change across a large organization. The first tactic is about continuous stakeholder engagement through value demonstration and capabilities building. Engagement is achieved by continuously providing information about proactive shortfall and opportunity identification within the installed asset capacity. Monthly asset reviews provide the basis for user interaction and initiate the basis for establishing ad-hoc production maximization scenarios. Establishing a data governance and performance metrics were also key for embedding the solution in the business processes. The solution delivers tangible and intangible value. From the tangible point of view, it contributes to production efficiency gains by compensating during specific proactively identified shortfalls and after-the-fact events. As a result, our solution has been instrumental in deriving cost reduction scenarios and profitability gains due to optimum GOR management. In addition, the system use has reported various intangible gains in terms of better data utilization, enhanced corporate database quality and reduced overall human load in managing production capacity. The solution described in the paper implements a simpler way the production planning and performance management at corporate level in a large integrated operating company. The in-house developed tool and its implementation is a novel approach in terms of integration, complexity, and practical application to the fields in Abu Dhabi.


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