The year 2008 has witnessed unprecedented fluctuations in the oil prices. During the first three-quarters, the oil price abruptly increased to $140/bbl, a level that has never been reached before; then because of the global economic crisis, the price dramatically plunged to less than $50/bbl by the end of the year losing more than 64% of the maximum price in less than three months period. The supply of crude oil to the international market oscillated to follow suite according to the law of supply and demand. This behavior affected oil production in all exporting countries. Nonetheless, the demand for crude oil in some developing countries, such as China and India, has increased in the past few years because of the rapid growth in the transportation sector in addition to the absence of viable economic alternatives for fossil fuel. The rapid growth in fuel demand has forced the policy makers worldwide to include uninterrupted crude oil supply as a vital priority in their economic and strategic planning. Even though forecasting should be handled with extreme caution, it is always desirable to look ahead as far as possible to make an intellectual judgment on the future supplies of crude oil. Over the years, accurate prediction of oil production was confronted by fluctuating ecological, economical, and political factors, which imposed many restrictions on its exploration, transportation, and supply and demand. The objective of this study is to develop a forecasting model to predict world crude oil supply with better accuracy than the existing models. Even though our approach originates from Hubbert model, it overcomes the limitations and restrictions associated with the original Hubbert model. As opposed to Hubbert single-cycle model, our model has more than one cycle depending on the historical oil production trend and known oil reserves. The presented method is a viable tool to predict the peak oil production rate and time. The model is simple, accurate, and totally data driven, which allows a continuous updating once new data are available. The analysis of 47 major oil producing countries estimates the world's ultimate crude oil reserve by 2140 BSTB and the remaining recoverable oil by 1161 BSTB. The world production is estimated to peak in 2014 at a rate of 79 MMSTB/D. OPEC has remaining reserve of 909 BSTB, which is about 78% of the world reserves. OPEC production is expected to peak in 2026 at a rate of 53 MMSTB/D. On the basis of 2005 world crude oil production and current recovery techniques, the world oil reserves are being depleted at an annual rate of 2.1%.

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Accurate formation fracture gradient prediction is an essential part of well planning. Erroneous fracture gradient estimates may jeopardize the entire drilling operation and result in serious well problems, the least of which are lost circulation and kick leading to blowout. Accurate fracture gradient values play an important role in the selection of proper casing seats, prevention of lost circulation and planning of hydraulic fracturing for the purpose of increasing well productivity in zones of low permeability. Furthermore, a good knowledge of the fracture gradient is of great importance in areas where selective production and injection is practiced. In such areas the adjacent reservoirs consist of several sequences of dense and porous zones such that, if a fracture is initiated during drilling or stimulation, it can propagate and extend, establishing communication between hydrocarbon reservoirs and can extend to a nearby water-bearing formation. Fracture gradient depends upon several factors including magnitude of overburden stress, formation stress within the area and formation pore pressure. Any prediction method should incorporate most of the above factors for a realistic prediction of the fracture gradient. This paper presents an artificial neural network model that yields reasonably accurate values of the fracture gradient. The input training data are actual field data. The results obtained from the model are compared with those obtained from correlation. The comparison shows that the method is promising and under some circumstances it is superior to the available techniques. Introduction Prediction of fracture pressure gradient plays an important role in designing safer drilling operations and economical well planning. The fracture pressure gradient is defined as the pressure gradient that causes fracture of the formation. In other words, if a formation is exposed to a pressure higher than its fracture pressure limit, the formation will fracture and a loss of circulation will occur. This condition may lead to problems varying from well collapse to gas kick followed by underground blowout. The consequences of an underground blowout are unpredictable. These aspects make formation fracture pressure knowledge fundamental when drilling oil wells. One of the main problems faced in predicting fracture gradients is the lack of data. In most cases only the leak-off test data, which frequently may have questionable results, is available to the drilling engineer. Fracture pressure gradient can be measured using either ‘direct’ or ‘indirect’ methods. The direct method relies on determining the pressure required to fracture the rock and the pressure required for the resulting fracture propagation. The indirect method relies on stress analysis or correlations to predict fracture gradient. The direct method is generally based on leak-off test (LOT) data. During a leak-off test mud is used to pressurise the well until the formation fractures. A leak-off test is a normal procedure in wildcat wells where the formation fracture gradient is not previously determined. However, if the area is very well known and the casing design requirements are not difficult to achieve, quite often the pressure test is terminated before reaching the formation fracture pressure. LOT may be a ‘dynamic’ or ‘static’ process. In dynamic LOT, pressure and volume are recorded while pumping the fluid. In a static test small amount of fluid is pumped in the borehole and the pressure is allowed to stabilise before recording. In addition to the operators' practices there are many factors that greatly affect the results of a leak-off test. Some of these factors include:Inaccuracy of equipment (gauges and pumps),Misinterpretation of the leak point,Lithology changes, andMud properties.