Video: Application of Kalman Filter to Predictions of Pore Pressure While Drilling

Bektas, Evren (University of Tulsa) | Miska, Stefan Z. (University of Tulsa) | Ozbayoglu, Evren M. (University of Tulsa) | Yu, Mengjiao (University of Tulsa) | Takach, Nicholas (University of Tulsa) | Velazquez-Cruz, David (Instituto Mexicano Del Petroleo) | Shahri, Mojtaba Pordel (Weatherford)


Accurate formation pore pressure prediction is the most important requirement for safe and effective drilling. It essentially contributes to the reduction of drilling risks and provides cost-effective drilling of wells. Even though there are methods that rely on well logs, seismic data and effective stress models to predict pore pressure, there is no model available for real-time pore pressure prediction ahead of the bit.

This study explains how to design and apply a steady-state Kalman filter to predict real-time formation pore pressure optimally by combining outputs from both Eaton's model and from Logging While Drilling (LWD). The objectives of this study are to minimize the noise on pore pressure predicted from well logs (or LWD) measurements and to compute the next estimate of pore pressure ahead of bit using only the most recent measurement.

Data obtained from well logs usually have noise measurements. The noise measurements lead to changes of the results acquired from well logs. Therefore, uncertainties occur on the estimated pore pressure. The designed Kalman filter is a recursive data processing algorithm to produce optimal estimate of pore pressure ahead of the drill bit. The estimates tend to be more refined than those based on measurements from well logs alone.

First, Eaton's method, which is one of the most widely used pore pressure prediction methods, was modeled and represented in the state-space form required for Kalman Filter modeling. Parameters used in the Kalman Filter algorithm were also identified in state-space form. A computer code to implement the designed Kalman filter algorithm has been developed. Additionally, field data were used to evaluate the performance of the proposed algorithm.

Based on the results, it is concluded that Kalman filter can be a very effective tool for detecting overpressure zones to avoid drilling problems before entering such zones.

The most important achievement is that the developed algorithm can be used as a design tool in offshore and onshore drilling operations along with real-time LWD data to optimally estimate the formation pore pressure of the next interval ahead of the bit during the drilling operation.