Probabilistic Drilling-Optimization Index Guides Drillers To Improve Performance
- Adam Wilson (JPT Special Publications Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- September 2018
- Document Type
- Journal Paper
- 99 - 101
- 2017. Society of Petroleum Engineers
- 6 in the last 30 days
- 131 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||Free|
|SPE Non-Member Price:||USD 17.00|
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 186166, “A Novel Probabilistic Rig-Based Drilling-Optimization Index To Improve Drilling Performance,” by A. Ambrus, SPE, P. Ashok, SPE, A. Chintapalli, and D. Ramos, Intellicess, and M. Behounek, SPE, T.S. Thetford, SPE, and B. Nelson, SPE, Apache, prepared for the 2017 SPE Offshore Europe Conference and Exhibition, Aberdeen, 5–8 September. The paper has not been peer reviewed.
This paper proposes a metric for quantifying drilling efficiency and drilling optimization that is computed by use of a Bayesian network. The network combines the identification of drilling dysfunctions (i.e., vibrational modes), autodriller dysfunctions, and mechanical-specific-energy (MSE) tracking into a single, normalized quantity that the driller can use to help decide which control parameters to adjust. The driller may be provided with operational cones on a weight-on-bit (WOB)/rotary speed plot to assist in this task.
The method proposed in this paper combines real-time surface measurements available on a drilling rig, derived quantities such as MSE and bit aggressiveness, and formation data (e.g., rock strength) into a probabilistic framework capable of handling the inherent uncertainty in the data and the process. The measured and derived parameters are encoded into a set of probabilistic features indicative of either the location of a particular physical attribute or a trend/movement of the attribute. These features are used to infer the beliefs of various drilling dysfunctions as well as the belief of an optimal drilling condition. The end result is a drilling-optimization index calculated whenever a drilling activity occurs. Because of its holistic nature, this index factors in the presence of various dysfunctions as well as suboptimal drilling rates. Additional dysfunctions can be added to the index easily, and the Bayesian network is forgiving when some data is missing. The index can be integrated easily into a decision-support system for monitoring drilling performance and providing recommendations for improved efficiency. Fig. 1 shows a detailed flowchart of the method.
Processing of Real-Time Drilling Inputs
The method starts by reading real-time drilling parameters, such as surface torque, rotary speed, WOB, rate of penetration (ROP), differential pressure, and control set points. If different rig sensors report data at different frequencies, a time synchronization of the sensor measurements is performed to depict the trends of the data collected by the various sensors accurately as a function of time. Next, preprocessing of the data collected from the sensors is performed. Preprocessing steps include removing obvious data outliers, as well as null or missing values, and summarizing high-frequency data to one or a few data points.
The next step involves identifying the rig activity. If the rig activity is drilling (either rotating or sliding), the system proceeds to calculate the MSE, bit aggressiveness, and stick/slip-alarm magnitude using the collected sensor readings.
Feature Extraction for Detecting Drilling Dysfunction
Once the measured and derived drilling parameters are obtained, their instantaneous values and trends are converted into location and movement features. Each feature outputs a probability value.
|File Size||1 MB||Number of Pages||3|