Evaluation of Crude Oil Production Forecast Studies Using Statistical Analysis June, Shinichirou Morimoto National Institute of Advanced Industrial Science and Technology

1. Introduction 2. Objectives 3. Forecast of Crude Oil Production (1)Optimist and Pessimist (2) Basic criteria to distinguish the forecast 4. Method of Statistical Analysis (1) Categorization (2) Statistical Analysis 5. Result 6. Conclusion

Introduction Increases of crude oil price Major factors 1. Increasing global oil demand (China, India, etc) 2. Lack of OPEC’s production capacity 3. Lack of the USA’s oil refinery capacity 4. Crisis situation in the Middle East 5. Inflow of speculative capital into oil market Major factor is “Decline of oil production which is economically feasible” Peak Oil StudyCheap Oil Study Decrease in the supply of oil, will cause serious economic and social effects No significant economic or social effects by introduction of substitute fuels

Objectives 1. The trend of oil peak forecast studies. 2. The basic criteria to distinguish oil peak forecasts. 3. Major factors for these basic criteria. 4. Important tasks (challenges) for oil peak forecast. Evaluate oil peak forecast using statistical analysis What is… to answer these questions… New insights for oil peak forecast

Optimist and Pessimist Optimist Pessimist Definitions of Pessimist 1. Defined as “Pessimist” in journal, technical report, etc. 2.Pessimsitic opinion toward reserve growth. 3. Criticize optimistic opinion. 4. Specialty : Geology 5. Affiliation : Petro consultant, university 6. Data : IHS Energy data, Petro consultant company data Definitions of Optimist 1. Defined as “Optimist” in journal, technical report, etc. 2. Optimistic opinion toward reserve growth. 3. Criticize pessimistic opinion. 4. Specialty : Economy 5. Affiliation : Oil major, Oil company Research institute 6. Data: USGS data, BP data, OGJ data Reference: EIA （ John.H.Wood ） Reference: Colin.J.Campbell

Oil Peak Forecast Studies Opinion

Basic criteria Future increase in the oil supply-demand gap 1.Technological innovation Time lag between technological innovation (for oil extraction incl- uding EOR and substitute fuel), and increasing of oil demand. 2.Influence of crude oil price Correlation between crude oil price and cost of oil extraction (New oil field discovery). No serious problemCause serious effects Oil peak caused in USA and UK Increasing of oil recovery rate, 100% Reserves replacement rate

Categorization 1.Categorization1 Categorization based on experts’ theories which support in their analysis as major factors of the future increase in the oil supply-demand gap. Categorization O ： “Lack of upstream or downstream investment in equipment due to political factors” or “Large-scale introduction of substitute fuels in the market” Categorization P ： “Decline in economically feasible oil production” or “Geological limits of oil reserve growth due to increases in the cost of extracting crude oil” 2.Categorizatio2 Categorization based on experts’ organizations. Categorization C ： Oil majors or oil companies Categorization U ： Universities Categorization S ： Oil consultant companies Categorization R ： International organizations or public institutions 3. Categorization3 Data and data analysis methods used by experts. Categorization G ： IHS Energy data, Campbell data Categorization E ： BP statistics data, OGJ data, P50 mean estimated by USGS

Statistical Analysis Method 1.Categorization1 (1) Regression analysis of oil peak forecasts is applied using Explanatory variable x: Time of forecast Objective variable y: Result of oil peak forecasts (2) Coefficients of determination R 2 are compared. 2.Categorization2 (1) Only simple regression equation is used for analysis. (2) Statistical tests of differences in the slopes of the simple regression equations are applied. 3. Categorization3 (1) Variances of the oil peak forecasts is compared analyze the effects of the data and methods.

Result (Categorization1) Categorization O 1.Lack of upstream or downstream investment in equipment due to political factors 2.Large-scale introduction of substitute fuels in the market Figure 3. Result of Statistical Analysis (Categorization O) Linear increase in relation to the time of forecast

Result (Categorization 1) Converge around 2010 for forecasts made at later times Categorization P 1.Decline in economically feasible oil production 2.Geological limits of oil reserve growth due to increases in cost of extracting crude oil Figure 4. Result of Statistical Analysis (Categorization P)

Result (Categorization 2) Figure5-3 Result of Analysis (Categorization R) t value is below the significance level No difference in the slopes of the equations Figure5-1 Result of Analysis (Categorization C) Figure5-2 Result of Analysis (Categorization S) Figure5-4 Result of Analysis (Categorization U)

Result (Categorization 3) Variance ： 93.8 Variance ： Figure 6-1. Result of Statistical Analysis (Categorization E) Figure 6-2. Result of Statistical Analysis (Categorization G) Data and method do not have significant effects on oil peak forecasts

Conclusion 1. The basic criteria to distinguish oil peak forecasts. The theories which support the expert’s analysis as major factors of the future increase in the oil supply-demand gap. 2. The trend of oil peak forecast studies Two distinct tendencies of oil peak forecasts depending on the specialties and theories of experts. Converge around 2010 Linear increase in relation to the time Experts’ organizations and used data have no significant effects on these tendencies. 3. Important tasks (challenges) for oil peak forecast How to obtain objective insights that can contribute to formulating energy strategies from uncertain forecasts, is likely to be ever more important when proposing energy strategies in the future.