Concessions were predominantly used up until the late 1960s, when Production Sharing Agreements- or also called Production Sharing Contracts (PSA or PSC) - came into existence. They provide an alternative to concessionary systems for those foreign countries, whose constitutions stipulate that all mineral rights lie with the state, but who do not wish to exploit their hydrocarbon resource base on their own. Regulations, which govern the external oil and gas reserves reporting, give a high degree of latitude as to how a company interprets and applies those rules to reserve bookings. From a financial point of view, PSAs and concessions are quite similar; they are distinctly different though from a philosophical point of view. The concession owner holds title to the hydrocarbons in place, whereas in the PSAs the entitlement is not transferred to the contracting party. But it is the entitlement to reserves, which ultimately provides the right to book reserves. For concessions, the reserve booking procedure is very clear, since the concession owner owns the mineral rights, whereas in case of PSAs the situation gets much more complicated. This paper explores different types of such agreements. It further discusses the question, whether reserve bookings for internal and external reporting purposes are permissible under certain kinds of arrangements and what, if any, volumes can be booked. Two methods are applied: The working interest method and the economic interest method. The hazard in using the working interest method, lies in the fact, that the government production entitlement would be treated as a tax expense from the contractor's point of view. But the host government or their representing oil institution will book their share of profit oil/gas as well, leading to reported gross reserves exceeding those ultimately produced. It would be preferable if oil companies strive to apply the economic method uniformly. The economic interest represents the actual barrel entitlement and thus is comparable to reserve numbers from concessions or leases. As the investment community places great significance on reported volumes to gauge the company's financial strength and future growth potential, it would be desirable to achieve a higher degree of consistency in reserve booking industry-wide. Internally the company will profit by improving benchmarking in portfolio management decisions.

This paper presents the analysis of measured bottom hole pressure data for 45 hydraulic fracture treatments pumped in Alaska and California in the past three years. On 30 of these wells, bottom hole data was obtained from memory gauges deployed on the tubing or in side-pocket mandrels, while the other 15 wells had reflected pressure up an open annulus. The fluid systems were either delayed borate guars, or linear gel HECs. The paper starts with a comparison of these measurements with the Keck et al4 correlation. The results show that there are large systematic errors both in the magnitude and trends of the correlation. We illustrate the effects these errors have on net pressure analysis and on the success of executing these tip screen out treatments. The data also shows an unexpected transient effect in almost all the wells, whereby changes in surface pressure of 100's to 1000's of psi are found to change the bottom hole pressure by 10's or 100's of psi, respectively. We speculate on possible causes for this phenomenon, but conclude that additional work is needed for accurate predictions of bottom hole pressures during fracturing treatments. Introduction Accurate methods to predict bottom hole treating pressures of hydraulic fracturing treatments have long been hoped for in our industry, but still do not exist. Bottom hole pressures drive mini-frac analysis, net pressure analysis, history-matching with models, and so the stakes for accurate bottom hole values are quite high. Given that measuring bottom hole pressures in real time is quite costly, and therefore is rarely routinely performed, we rely on calculated values from correlations. Since the first use of polymer based fluids, the industry has recognized that accurate predictions are complicated by two fundamental problems. First, even non-crosslinked linear gels are highly viscoelastic, showing drag reducing properties in turbulent flow that we take advantage of to lower friction losses. No theory, however, exists that allows scale-up of small sized laboratory test results to field sized tubulars, and so we are forced to use correlations that try to extrapolate from limited data. The second problem is that the addition of proppant to these fluids further distances us from theoretical calculations and moves us more into reliance on correlations. A comprehensive review of the literature in this area is beyond the scope of this paper, and the reader interested in works published before 1990 might start with the SPE monograph by Gidley et al.1 or the review text by Economides and Nolte2. Since then, there have been several studies on friction pressures of water-based fracturing fluids, including, for example, linear gel friction pressures (Shah3, Keck et al.4), delayed borate fluid systems (Tan5), laboratory studies on proppant effects (Shah and Lee6, Keck et al.4), and field studies (Jennnings7, Bilden8).

The development of the natural gas industry in the last decade was shaped by deregulation from federal oversight. This created an environment with fierce competition but also highly volatile spot prices, which exposes market participants to a considerable price risk. Risk management becomes vital in such a business environment. An integral part of risk management besides understanding the market's fundamentals is to develop quantitative models which help predicting gas prices or price changes based on market information. Current price prediction focuses on long-term equilibrium price forecasts, but does not give any information about the day-to-day volatile behavior of spot prices. For long-term delivery contracts this might be sufficient, but for trading on a speculative basis more accurate information about price movements is sought. This paper shows that a solution for day-to-day prediction of spot prices is feasible. The most appropriate models in achieving this task were selected to be an econometric model with lagged variables and a neural network model. As volatility of the spot price is usually higher in the winter and seasonality in the consumption pattern is given, the model was developed based on data for the winter. The performance of both models was tested in a simulated trading scenario and compared to a scenario where perfect prediction quality was assumed. The results showed that both models were profitable during the time span the test was conducted. The neural network showed better results than the econometric model. In comparison to the best case scenario the results were very pleasing. These models can provide a valuable, supportive tool for trading of natural spot gas in a speculative environment.

This paper describes a systematic, consistent methodology to collect reserve and economic information for characterizing a company's oil and gas resources. The authors firmly believe value is realized, both internally and externally, by being able to effectively characterize a company's hydrocarbon resources. The methodology is comprised of four elements: resource definitions, tying resources to production forecasts, valuing resources, and assessing uncertainty. A data collection process is outlined so that the same data can be used for multiple purposes including long range planning, portfolio management, regulatory reporting, reserve management, technology planning, and establishing performance targets. Finally, implementation issues associated with developing a company hydrocarbon resource characterization are reviewed. The paper is a summary of processes developed by ARCO from 1998 to 2000 to better characterize company resources and manage its portfolio of assets. Introduction A well-established process for oil companies is estimating and reporting proven reserves. Historically this function was the responsibility of a central reservoir engineering group, which consolidated input from various operating assets. The central group would consolidate the information, review for consistency, and provide the summary for annual reports and SEC reporting. This process was independent and typically focused on proven reserves only. Long range business planning for capital allocation and budgeting was also an independent process, or at best, casually linked to the reserves process, although much of the underlying data was similar. Today a company is concerned, not only about proved reserves, but having adequate, well characterized hydrocarbon resources, scheduled to replenish current production and reserves. Although US reporting requirements focus only on proved reserves, having similar information on unproven reserves is necessary for proper management of a company's hydrocarbon resources. In addition, portfolio management techniques1 are increasingly being used to bring a better understanding of risk into the long range planning process. To be meaningful, these techniques require detailed, quantitative descriptions of the resource opportunities available to a company. These factors have driven the need to integrate the classic reserve reporting and long range planning processes. One aspect of this integration is having a consistent characterization of a company's hydrocarbon resource base for both processes to draw on. Such a characterization can serve multiple needs: long range planning, portfolio management, regulatory reporting, reserve management, technology planning activities, and establishing performance targets. The authors firmly believe value is realized, both internally and externally, by being able to effectively characterize a company's hydrocarbon resources. Active, consistent resource management adds value by:identifying stakes and rank projects not only economically but by risk,enabling the effective allocation of limited financial and human resources,more effectively conveying growth and profitability prospects to the financial markets Having high quality and consistent data is critical to properly characterize a company's resources.