The desire to move the industry to implement the digital oil field has never been greater, and new technologies are being announced daily. From edge computing devices to artificial intelligence and machine learning, the amount of data and analysis capabilities for the operator continues to grow. But deploying a solution to take advantage of this digital data can come with an abundance of technical and logistical challenges.

For operators planning to build or enhance their digital infrastructure, their organizations will need to overcome obstacles in some of the most unlikely places. The need to monitor wells, perform analysis and affect operational change to reduce costs and improve production and hydrocarbon recovery are key drivers behind the digital oil field push. Despite the best effort at planning, deployment issues only serve to deter adoption and field acceptance. Even with the continuing rollout of faster wired and wireless services, many wells are in remote locations with limited or no connectivity. Wells in good geographical locations or with reliable, high-speed access can also face significant technical hurdles. Integration of diverse digital systems from a myriad of suppliers while sustaining legacy data is challenging, despite industry efforts for standardized protocols. But how can you architect a viable and efficient solution while sustaining a compelling application experience across a scaling infrastructure?

This paper focuses specifically on how managing the movement and volume of data in smart and efficient ways can help create a successful implementation. It will provide some insight on how to overcome the logistical and technical challenges faced in real-world deployments. The paper will highlight one specific segment of the digital oil field, focusing on how new and long-standing distributed temperature sensing (DTS) installations can benefit from better integration and automation of data collection. The discussion will expand on how incompatible systems can be integrated into a digital workflow in a relatively inexpensive, but efficient process, and will reveal how evolving technologies such as distributed acoustic sensing (DAS) can compound the challenges, and what steps can be taken to lessen the potential impact faced by these newer capabilities as they become commercially available. The paper will culminate with examples of how a straightforward implementation can be deployed where there is no existing digital solution, how incompatible system data can be captured to provide meaningful information and how these systems can be used to form a part of a larger, intelligent completion design

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