چكيده به لاتين
Abstract:
Drilling operation is always full of risks and challenges as far as geological and operational aspects are concerned. These issues can lead to major operational delays, change of plan or drilling operation failure. In fact, realistic and precise timing is the key to a successful drilling planning. Yet planning cannot be practical if operational challenges and their effects are not accounted for.in this study following acquisition of expert views through questionnaires among all drilling operation risks in the probability-impact matrix. The effect of main risks of oil well drilling are identified. Data of predicted timeframe, real timeframe, and delay time resulted from dangerous risk factors were collected from 120 oil wells. The data were then fed into an artificial neural network which could successfully model the data with 95% accuracy. The network was then used to run sensitivity analysis on how different risk factors could affect the timing of the project, considering the delay in oil production, based on economic criteria of net present value and internal rate of return. The main goal was to prioritize risks and also to help present an integrated risk-oriented drilling operation plan. The condition of the drilled hole, drilling fluid loss, equipment problems and fishing were found to be the most effective risk factors, which must be considered during planning and risk analysis phases. Using the time estimated by the neural network and the identified risk factors, the drilling operation progress can be predicted with greater accuracy, since risk factors are integrated into the operation. In conclusion, after determining the four main risk factors and via the use of the trained neural model, the predicted timing and progress chart of the project can become more realistic. This, in turn, would translate into more precise cost estimation. After carefully investigating the data from 10 oil wells, the average of prediction error was reduced from 24.2% to 2.8%. This indicates how the model can help improve integrated risk-based planning of drilling operations.
Keywords: Drilling planning, Risk, Drilling progress chart, Neural network
