An important aspect of reserves estimates is to quantify the contribution of uncertainties in underlying parameters such as structure, Sand extent, Fault Seal, Aquifer support, etc. If field production history is available, it can be incorporated to generate reliable measures of uncertainties for these parameters and identify the most likely field models. History Matching (HM) can be solved successfully using the Design of Experiment (DOE) method to define modeled scenarios. In the past decade DOE methodology has been used to compute estimated reserves while minimizing the simulation effort. This paper describes its extension to History Matching.The process of HM using DOE is a four stage process. The first step is to identify an uncertainty frame-work listing the uncertain parameters (X) and history matching parameters (Y). The second stage is to generate a design (the specified model combinations of the reservoir parameters) capable of estimating the effect and interactions of all parameters. Thirdly, the specified models are made and simulated and the response surface y(x) calculated, relating reservoir simulator input x and each output y. The last step is to predict the history matching parameter values for all possible model scenarios and measure goodness of history match for each model.The History Match can be quantified by the D-Score, a measure of the difference between the realized and predicted value of the history matching parameter standardized across modeling error. The D-score based probabilistic measures are flexible and allow the user to incorporate prior information.The above methodology for History Match will be presented for a field, which has over 11 years of oil production and 7 years of water-cut. We will demonstrate that History Matching using DOE is reliable, fast, efficient, and can quantify the probability of scenario occurrence.

Efficient allocation of shelf space and product assortment can significantly improve a retailer's profitability. This paper addresses the problem from the perspective of an independent franchise retailer. A Category Management Decision Support Tool (CMDST) is proposed that efficiently generates optimal shelf space allocations and product assortments by using the existing scarce resources, resulting in increased profitability. CMDST utilizes two practical integrated category management models that maximize the total net profit in terms of decision variables expressing product assortment, shelf space allocation, review period, and order quantity. The implementation of the models demonstrates their robustness and that the net profit can be significantly increased when compared to the current industry practice.

Probabilistic assessment of reserves is commonly performed through a series of reservoir simulations over the range of field parameters. In most cases, an exhaustive study of parameter combinations is unfeasible because the number of parameters to be investigated is usually large. Statistical design of experiments (DOE) can be used to select a small number of reservoir simulation runs. The choice of design, especially its ability to capture complex interactions in field parameters, is crucial for DOE to be successful. To this end, we have developed a Scenario Analysis Tool (SAT) using DOE methodology for selecting scenarios to run in 3-D modelling and analyse the resulting reserves values to estimate the ultimate recovery. The primary goal of SAT is to minimise experimental effort (and cost) by recommending the minimum number of reservoir simulation runs required to accurately estimate uncertainty in hydrocarbon reserves for a given set of uncertainty parameters. The process has three main steps:specify the uncertainty framework to generate appropriate scenarios;model scenarios in 3-D simulators and input the response into SAT; anduse SAT to perform the analysis and generate the probabilistic response curve. SAT is implemented within the Microsoft Excel environment, making it user friendly with simple and intuitive data entry and minimal training requirements. The user is guided by menus and dialog boxes through a few simple sequential steps. SAT output includes reserves estimates, probabilistic response curves, tornado charts, and a comprehensive statistical output. Efficient designs results in a significant reduction in simulation effort for generating the reserves value. Usually, less than 0.1% of possible models need to be simulated and confidence levels are beyond exploration teams' expectations. The capability of SAT will be demonstrated through a number of examples. Introduction In reservoir uncertainty studies 3D geostatistical modelling is performed to quantify the distribution of the output response - for example, hydrocarbon-in-place volume and ultimate recovery. An important question to answer is: "Which scenarios should be selected for modelling?" Numerically, simulating a large number of 3D models is not practical, so it is important to select a small subset of models, making sure that we capture the significant effects due to key uncertainties. Using the statistical Design of Experiments (DOE) technique can significantly reduce the number of 3D models used to properly quantify the output response and its probability distribution curve. DOE has been successfully practiced since the 1930's in a broad number of fields, such as agriculture, biology, and chemistry. Using DOE methods in the oil and gas industry can be broadly classified into three categories:sensitivity studies;prediction; andoptimisation.

The growing human population in Africa is putting increasing pressure on habitats and wildlife outside of protected areas. The wildlife conservancy model in Namibia empowers rural communities to decide on the use of wildlife. Namibia started to implement the conservancy model in the 1990s and provides relevant experience from which other countries can learn. We reviewed the conservancy model in northwest Namibia to identify lessons for other countries. Our core work included case studies on six conservancies. We confirmed success factors for conservancies include: investment and revenues, strong governance and support from NGOs, as has been identified in previous studies. We conclude that a comprehensive wildlife monitoring programme is also a critical success factor. The wildlife monitoring method in conservancies in Namibia has been consistent since 2001, and the results show that populations have recovered and stabilised, although there are ongoing risks to wildlife and habitats in this fragile landscape.

A retail category management model that considers the interplay of optimal product assortment decisions, space allocation and inventory quantities is presented in this paper. Specifically, the proposed model maximizes the total net profit in terms of decision variables expressing product assortment, shelf space allocation and common review period. The model takes into consideration several constraints such as the available shelf space, backroom inventory space, retailer's financial resources, and estimates of rate of demand for products based on shelf space allocation and competing products. The review period can take any values greater than zero. Results of the proposed model were compared with the results of the current industry practice for randomly generated product assortments of size six, ten and fourteen. The model also outperformed the literature benchmark. The paper demonstrates that the optimal common review period is flexible enough to accommodate the administrative restrictions of delivery schedules for products, without significantly deviating from the optimal solution.