The objective of the study summarized in this paper was to establish a robust method to detect outliers in rate and pressure data used for decline analysis and forecasting, pressure and rate transient analysis, and similar workflows. Presence of outliers contributes significantly to the uncertainty and non-uniqueness associated with the rate/pressure transient analysis workflow.
In time-series data usually encountered in rate/pressure transient analysis workflows, we observed that the neighboring data points of an inlying data point will be closer to the point as compared to closeness of an outlier data point. The outlier detection method we developed exploits this observation. It relies on the concept of local density around a data point, and locality is quantified by a function of distance of the data point from its k-nearest neighbors. This concept further maps into a local outlier factor, which signifies the quantitative extent of a data point being an outlier. Outliers will have a higher value of local outlier factor than will inlying data points. A threshold value of local outlier factor is used to label the data points as outliers, and this threshold is set using the histogram of local outlier factor values.
Most commonly used outlier detection methods in the industry assume that rate/pressure data follow a known model (e.g., a Gaussian probability distribution model), but outliers deviate strongly from this model. In the usual parametric-model-based approach, an a priori assumption of the correct model needs to be verified, which can limit the validity of the approach. The newly proposed outlier detection method, being non-parametric, is more robust as it is independent of the assumption that data follows a known model.
We present a synthetic case study demonstrating the importance of outlier detection and outlining the limitations of currently used outlier detection methods. We then validate our method through synthetic examples generated using numerical models of multi-stage hydraulically fractured wells in unconventional reservoirs. Upon validation we demonstrate application of our method using field examples from four major shale plays.