Delays of project development have become one of the major concerns in the upstream oil and gas industry. An accurate projection of project timing is very important in production forecasting and project economics.

Statistical analysis shows that Time from Discovery to Production (TDP) has lognormal distribution, and thus, behaves like a random variable (Hattis and Burmaster, 1994). Using the historical average TDP to predict TDP of a new discovery is the same as predicting stock prices with history price data, and therefore, is not reliable, based on the random walking theory (Malkiel 1973), which states that the past movement or direction of the price of a stock or overall market cannot be used to predict its future movement. Operators have optimistic bias in their own project development timing estimation, and this becomes one of the major causes of project delays. The random nature of TDP and the operator's optimistic bias regarding project timing could mislead both investors in project economic valuation and contractors in market planning.

In order to increase the accuracy and objectivity of the project timing estimation and to predict project delays, this paper analyzes the First Production Time (FPT) estimation of oil and gas upstream projects by introducing a stage based multi-factor model in a dynamic environment. In this model, projects will be divided into seven different stages in the field development life cycle, which include Exploration and Discovery, Appraisal, Conceptual, FEED, EPC Bidding, EPC, and Producing. Then, the model will allocate estimated time to each stage including the gaps in between stages based on historical records and a set of factors. These factors include discovery commerciality, development feasibility, geopolitics, global economy, water depth, operators/partners strategy, project financing, contractor capacity constraints, etc, which would affect the project timing in specific stages.

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