Rate of Penetration Optimization using Moving Horizon Estimation

Abstract

Increase of drilling safety and reduction of drilling operation costs, especially improvement of drilling efficiency, are two important considerations in the oil and gas industry. The rate of penetration (ROP, alternatively called as drilling speed) is a critical drilling parameter to evaluate and improve drilling safety and efficiency. ROP estimation has an important role in drilling optimization as well as interpretation of all stages of the well life cycle. In this paper, we use a moving horizon estimation (MHE) method to estimate ROP as well as other drilling parameters. In the MHE formulation the states are estimated by a forward simulation with a pre-estimating observer. Moreover, it considers the constraints of states/outputs in the MHE problem. It is shown that the estimation error is with input-to-state stability. Furthermore, the ROP optimization (to achieve minimum drilling cost/drilling energy) concerning with the efficient hole cleaning condition and downhole environmental stability is presented. The performance of the methodology is demonstrated by one case study.

Real-Time Minimization of Mechanical Specific Energy with Multivariable Extremum Seeking

“…Selecting the optimal WOB and RPM is not a trivial task. Available drilling models might not be accurate enough in predicting the relationship between the ROP and related drilling parameters [15,16]. Varying downhole conditions such as changes in pore pressure or formation properties as well as degradation of the bit teeth/cutters can alter drilling efficiency so that the combination of WOB and RPM that was optimal a short time ago might no longer be the best solution.…”

“…The main advantage of applying the ES method for drilling optimization is that it is model-free, and therefore requires limited a-priori knowledge about the current drilling environment to be employed. When using models to predict how to drill optimally [8,9,15,16,27], the models need to be tuned based on data that is representative of the current downhole conditions. When the conditions change, the models will no longer be valid before they are re-tuned to the new circumstances, which can limit their applicability for real-time optimization.…”

Micro-Testing While Drilling for Rate of Penetration Optimization

“…The tuned models are used to predict how the drilling process will react to different values of the controllable parameters such as WOB, drill string rotational speed (RPM) and flow rate. Based on this prediction, the models can be used to provide estimates of the optimal drilling parameters, which can be supplied to the driller as suggestions or directly fed to the control system on the rig in a closed loop [1][2][3][4][5]. Field tests of an ROP optimization algorithm using physics-based models have shown good results, with the largest increases in ROP obtained when the algorithm was run in closed loop [2,3] and a reduction in downhole tool failures when applying the optimization algorithm [3].…”

“…A potential drawback to real-time optimization with the physics-based models is that the analysis is based on a mathematical description of the drilling process, and the existing models might not be very accurate in predicting the ROP [5,6]. A possible remedy for model inaccuracies could be the use of data-driven modelling techniques, or a hybrid between datadriven and physics-based modelling methods.…”

“…A changepoint algorithm which determines what historical data is relevant for the task of ROP modelling and optimization has been implemented [2]. Using a sliding window of data containing a fixed amount of the most recent measurements to tune a physicsbased drilling model has also been suggested [4,5]. In addition to using representative data, the models need a varied sample of input (e.g.…”