The global economy continues its journey of evolution and progression driven by industrialism as its primary force. With such a fast pace of development and recovery from several recessions over a number of years, dependency on energy sources became inevitable to satisfy the rising demand. This paper represents a proposed global energy price model that has the flexibility of modeling the energy price, using data from specific regions of the world, as well as the global energy pricing equation. The ANM (Alternate Novel Model) is presented here.
The model focuses mainly on oil price modeling, since oil accounts for more than 84% of the current world energy supply. The model duration is 50 years; starting from 1980 to 2030, model matching period from 1980 to 2011, and the prediction period is from 2012to 2030.
The modeling approach used in ANM adopts weighted averaging of individual factors and it relies on line regression technique. Therefore, future trends are being predicted based on the cyclic nature of the market and historical data "the future is reflection of the past??. ANM can then preduct the future oil prices, depending on the factors and variables that have been placed in the process for the output results.
The paper aims to propose a reliable model that accounts for most governing factors in the global energy pricing equation. All steps followed and assumptions made will be discussed in detailto clarify the working mechanism for this model and pave the road for any future modifications.
Alusta, Gamal Abdalla (Heriot-Watt University) | Mackay, Eric James (Heriot-Watt University) | Collins, Ian Ralph (BP Exploration) | Fennema, Julian (Heriot-Watt University) | Armih, Khari (Heriot-Watt University)
This study has focused on the development of a method to test the economic viability of Enhanced Oil Recovery (EOR) versus infill well drilling where the challenge is to compare polymer flooding scenarios with infill well drilling scenarios, not just based on incremental recovery, but on Net Present Value as well.
In a previous publication (Alusta et al., 2011, SPE143300) the method was developed to address polymer flooding, but it can be modified to suit any other EOR methods. The method has been applied to a synthetic scenario with constant economic parameters, which has demonstrated the impact that oil price can have on the decision making process.
The method was then applied and tested (Alusta et al., 2012, SPE150454) with varied operational and economic parameters to investigate the impact in delaying the start of polymer flooding to identify whether it is better to start polymer flooding earlier or later in the life of the project. Consideration was also given to the optimum polymer concentration, and the impact that factors such as oil price and polymer cost have on this decision. Due to the large number of combined reservoir engineering and economic scenarios, Monte Carlo Simulation and advanced analysis of large data sets and the resulting probability distributions had to be developed.
In this paper the methodology is applied to an offshore field where the choice has already been made to drill infill wells, but where we test the robustness of the method against a conventional decision making process for which there is historical data. We do this by performing calculations that compare the infill well scenario chosen with a range of polymer flooding scenarios that could have been selected instead, to identify whether or not the choice to drill infill wells was indeed the optimum choice from an economic perspective.
We conclude from all the reservoir simulations and subsequent economic calculations that the decision to drill infill wells was indeed the optimum choice from an economic perspective.
Both oil and gold are commodities with price in US Dollars, but they choose different path in trend figure. While gold has been showing great stability over the years, oil keeps changing in price level. Oil price movements have distorted measurement of economic variables measured in dollar values. In economical evaluation for oil and gas field development projects longer than one year, oil price is one of the most critical assumptions.
This paper is trying to solve whether:
• gold is more stable than US dollars or other currencies
• gold equivalency is more reliable way to project the future costs/price
• the gold-based oil price can be applied in current economical evaluation template for justification of approval process on field development plan
Considering crude oil prices are moving dynamically for last decade, this paper exercise the model to determine realistic oil price assumption by using more stable "currencies??, thus it can provide more reliable and accurate economical evaluation. It shows that gold-based inflation-adjusted crude-oil price is preferable indampening or mitigating:
• effect of dynamic oil price nature
• impact of inflation
• risks of paper-based currency fluctuation
• discount rate requirement
Using case study of Indonesian Production Sharing Contract (PSC) fiscal terms, gold-based oil price provides more simple economical evaluation, resulting real net cashflow of field development plan. The paper concludes by demonstrating using gold equivalency instead of paper-based currencies provides more consistent and reliable nominal revenue in both perspective of PSC Contractor and Government.
The subject Gas Field is located in the Sulaiman Fold Belt (SFB) in Pakistan. A realistic 3D static model was constructed for the challenging multiple reservoirs in the Field which included both clastics and carbonates. Four main reservoir horizons were modeled.
The steps involved in the Reservoir engineering analyses were: analyze PVT, well test, Static Pressure Data, and Core. The static pressure analysis helped define hydraulic compartmentalization in the field.
WHFP measurements were not available in the desired accuracy and density. A surface network model was used with plant inlet pressure as the primary constraint in order to obtain the required information. Satellite based elevation information was used to establish an accurate model with respect to pressure drop due to liquid hold up in pipelines.
The History Match in the field was performed on a Zone by Zone basis. In the absence of a 3D seismic cube, many of the faults in the field could not be interpreted, yet their presence was predicted by a closely matching Sand Box Model. This was an important clue which led to a useful approach regarding the location of simulation faults distributed in the entire field. An innovative approach was used in order to calibrate the size of sand lenses in one of the zones.
The final step was the forecasting and development of Optimal Scenario using Economic analysis. Many scenarios were tested, and the optimal scenario was identified. Maximum use was made of existing wellbores through re-completion, and new drilling was minimized. Furthermore, the impact of increasing the currently low Gas Price was tested. It was concluded that doubling of the gas price of the field would increase the NPV 3 times delay abandonment by 6 years.
The Gas Field is located in the Sulaiman Fold Belt (SFB). Eighteen (18) wells in all, those have been drilled in the Field. Currently 12 wells are producing Gas. The primary target horizons in Field are the Sui Main Limestone (SML) and Lower Ranikot (LRKT). However, the Dunghun Limestone and Pab Sandstone are also producing in some of the wells. The depositional sequence consists of clastic and carbonate succession. The stratigraphy of the reservoirs is strongly influenced by the structural evolution of the Sulaiman Fold Belt and initial rifting of the Indian Plate.
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Petroleum producers are currently engaged in significant expenditure towards exploration and development in unconventional oil and gas plays in Western Canada. Finding & Development costs are different in various plays and strategies. But where the producer should focus their resources to achieve most cost efficient production?
The paper describes methodology of analysis of Finding & Development cost. A probabilistic model was developed to quantitatively assess exploration and development expenditure, production, and reserves for various producers for different oil and gas plays. The model employs a number of key performance indicators (KPIs) such as Finding & Development costs with and without acquisitions, reserves life, reinvestment, and others.
The methodology was applied to a comprehensive study of Finding & Development expenditure in Western Canada focused mostly on unconventional oil and gas. The study included more than 80 oil and gas companies. Each company may be involved in exploration and development of many plays. The expenditure, production, and reserves were analyzed for the last 12 years. The companies were subdivided into three groups based on their production. Each company's Finding & Development costs and other KPIs were calculated for Western Canada as a whole and for a particular strategy or play where the company was operating, as well as for CBM, tight, and shale gas.
The study found significant variance in finding and development cost in Western Canada. All companies and all strategies are ranked based on their Finding & Development costs and other KPIs. The results of the study can be applied to the comparative analysis of efficiency of the exploration and development expenditure, which in turn can help improve portfolio management and decision making processes.
In 2005 a series of statistical calculations were presented for Canadian hydrocarbon prices . There were two main conclusions: long-term historical data indicates that hydrocarbon prices tend to revert back to historical averages, short-term price
fluctuations are unpredictable. It is more clear than ever that short-term prices are unpredictable, but this paper will attempt to demonstrate once more that mean reversion should be included in any long-term model.
This paper demonstrates that any discussion of oil and gas prices in Canada must consider inflation. Several different means of adjusting for inflation are presented but all show that Canadian hydrocarbon prices are strongly variable, but mean reverting. This paper also argues that, while convenient, discussing the price of a commodity in terms of only one currency ignores changes in the relative value between currencies and basis differentials. These factors can have significant economic impact.
This paper updates the previous price fluctuation model for prices up to the end of 2012. As before, the model incorporates a random walk with mean reversion that was developed and tuned to fit Canadian hydrocarbon prices. Starting with the current spot price, the model will generate a random but equiprobable prediction of future prices. The model can be used as input into a Monte-Carlo simulation. Alternately, the model can be run multiple times in order to generate "high??, "low??, and "expected?? price predictions.
Economist's Corner - The oil and gas industry is unique: It is subject to constant scrutiny and regulation from multiple angles. It consists of two distinct cultures, one in the corporate setting and the other in the field. It can also be exceptionally lucrative at both the company and investor level. In addition, the oil and gas sector differs from other industries in the way energy companies are valued by the financial community.
Long-term economic viability of unconventional reservoirs is evaluated from the profit-maximizing perspective of a producing company. The case of the liquids-rich production from the Bakken field is considered as a representative of unconventional resources. A profit-margin optimization model is constructed for a company to meet the demand it faces from a stock of conventional and unconventional resources given different sets of exogenously determined prices. The model is parameterized using the different production decline rates of the two sources, physical and economic exhaustibility of the resources, and the ever increasing marginal cost of adding conventional resources into the company portfolio. The optimal extraction path of oil from the conventional and unconventional reservoirs is assessed, and the long-term economic consequence of keeping the unconventional resource in the ground for different oil-price scenarios is predicted. The model reveals the appropriate composition of a portfolio of conventional and unconventional resources. In the case of a high-price scenario, the optimal efficient extraction path is the pursuit of additional conventional resources before using unconventionals to meet the demand. For the reference-price scenario, the decline of the conventional reserves should be substituted with unconventionals from the beginning. The profitability of the enhanced oil recovery (EOR) applications in unconventional reservoirs and when they should be implemented are also determined. Contrary to common expectation, it is shown that the EOR technology is more justifiable in the case of a lower price forecast.
Since the early days of the petroleum industry, prediction of oil prices has been a real challenge. The puzzling question we need to answer when evaluating project's NCF is: how much is the price of a barrel during the life-span of the project? Accordingly, oil price modeling became a vital tool to predict both short- term and long-term prices. Unfortunately, there are many uncertainties associated with the available models and none of them can predict oil prices with acceptable accuracy. Only limited controlling parameters are captured by these models. These parameters are basic and derived from simple assumptions of supply and demand dependency. Nowadays, the need for a reliable oil price model became more critical as a change of oil price is experiencing dramatic fluctuations that affect economic decision parameters a great deal.
This paper presents an oil-price model to project the price behavior in the next 20 years. Different scenarios were examined out of which "Economic-Scenario?? was found to be the best suitable predictor. This model takes into account multiple effects of fourteen parameters that are believed to have the highest impacts on oil price. These factors have been further classified into key categories such as supply, demand, reserve and externalities (political/environmental/social) which is regionally based. Other parameters such as population growth and technology are embedded within these key factors. According to this model, oil price has been found to have strong reliance on the US Dollar and inflation, which has been incorporate into the model to ensure a more reliable outcome.
Market behavior modeling is a continuous process which is planned to be integrated into the proposed model in the near future once consistent data become available. The major obstacle in modeling market behavior is the lack of futuristic behavior that is primarily dependent on accurate historical data. This data should reflect the performance of short-term effects such as lifestyle, human behavior, politics, conflicts, wars, natural disasters, environmental issues and other economies' behaviors. The ultimate goal of this modeling effort is to assist in economic and risk analysis evaluation of petroleum projects.