Technology Focus: Production Operations (March 2013)
Authors: Alhanati Francisco J.S. 
Published: February 2015

Technology Focus

Production engineers are often tasked with analyzing existing data to try to understand the factors affecting equipment failures or subpar performance, to guide the implementation of actions geared toward mitigating such problems and improving overall operations. Sometimes, this can become quite a difficult task.  

One reason is that the existing data can be of bad quality, containing records that are incomplete (missing critical information), inconsistent (with information incompatible with other information in the same record or in other records), or inaccurate (because of errors in measuring or recording the values for the parameters). Sorting through such issues with the data can sometimes be very time consuming. It often is not entirely successful; and it is never fun. 

Another reason is that evaluating the effect of each possible influential factor is usually more complex than we first think. Early on in my career, I came across a quote, attributed to Sir Cyril Hinshelwood, in a book about data reduction and analysis, which I share often with my younger colleagues. It describes the normal stages of developing a theory on the basis of existing data: The first stage usually involves “gross simplifications, reflecting partly the need for practical views and even more a too-enthusiastic aspiration for the elegance of form.” In the second stage, “the symmetry of the hypothetical system is distorted and the neatness marred as recalcitrant facts increasingly rebel against uniformity.” In the third stage, “if and when it is obtained, a new order emerges, more intricately contrived, less obvious, and with its parts subtly interwoven, since it is of nature’s and not of man’s conception.” Putting the puzzle together is the fun part. 

Some of the papers in this feature illustrate how tough production challenges can be tackled on the basis of thorough analysis and interpretation of good-quality data. Collecting such good-quality data has a cost. If collecting the data is worth the effort because of the value that we can extract from the information it contains, then implementing measures to ensure its quality should also be warranted. It will allow us to spend our time in the most rewarding (and entertaining) part of the task, which is coming up with a good theory for the trends we can see in the data.

Recommended additional reading at OnePetro: www.onepetro.org.

SPE 153005 An Exhaustive Study of Scaling in the Canadian Bakken: Failure Mechanisms and Innovative Mitigation Strategies From More Than 400 Wells by Jonathan J. Wylde, Clariant Oil Services, et al.

SPE 159385 Paraffin-Deposition Analysis for Crude Oils Under Turbulent-Flow Conditions by Hamidreza Karami Mirazizi, The University of Tulsa, et al.

SPE 153224 Coiled-Tubing Sand Cleanout at Low-Bottomhole-Pressure, Large-Diameter-Casing, and Long-Horizontal-Well Applications in Deepwater West Seno Field by J.B. Putra Koesnihadi, Chevron, et al.

OTC 23622 Treating and Releasing Produced Water at the Ultradeepwater Seabed by Timothy P. Daigle, Fluor Offshore Solutions, et al.