In 2009 Statoil rolled out a performance incentive business model ensuring high quality real-time drilling data deliverables [SPE 127799]. But how do you measure performance? And how do you create a system automatically monitoring quality all the way from the rig and into target systems onshore?Talking to operators and service companies, Statoil concluded that our industry seemed to be at a starting point when it comes to deploying automated monitoring systems targeting both availability and data content.A technology development project was established in parallel with the performance incentive business model [SPE 127799], with the following goals:1. Ability to monitor real-time drilling data streams from Statoil's drilling operations into all target systems 2. Automated alarms triggered by data content automatically notifying operators on e-mail/SMS

WITSML is a key enabler in an increasing number of real-time workflows. This is particularly true for integrated operations within the growing numbers of onshore operations centers. Two years ago WITSML was a technology known by few and actively used by even less. Now the SIG steering the standard has grown to 51 companies. The starting point for most companies in using WITSML is to bring depth data into their asset databases. For many this has become the norm. Early adopters like Statoil are broadening their use into a wider range of wellsite operations. Within asset teams a standard data delivery mechanism allows integration of new tools and workflows, letting Geologists and Engineers make use of real-time data within their familiar desktop applications. It also enables centrally managed data delivery services letting them focus on their areas of expertise, not data gathering. New technology and processes in these areas are helping operators make the next big step from real-time remote monitoring to real-time remote control. For growing numbers it is now not enough to receive a visual representation of the data. They expect data to be delivered in real-time via a standard format. WITSML also brings operators the opportunity to standardize data delivery workflows, to clarify contractual requirements to providers and to establish and measure realistic data delivery KPIs. Within Schlumberger WITSML enabled workflows are well established, answer products for drilling optimization and interpretation utilize the standard via a unified WITSML client. This reduces software development cycle time and simplifies data gathering. WITSML is becoming established, bringing proven advantages to real-time workflows. Continued uptake of the standard will enhance the competitive advantage of service companies and the operators utilizing it. WITSML is here to stay and should be supported more widely within the industry. Introduction The Wellsite Information Transfer Standard Markup Language (WITSML) is a data transfer standard designed to facilitate the efficient and effective flow of drilling data between the wellsite and the office. The WITSML standard developed out of WITS (Wellsite Information Transfer Standard) (Jantsen et al. 1987), which has been widely used since the early 1980's. Using the standard, object oriented data is transferred as XML documents over SOAP and HTTP/S (Kirkman et al. 2003). This data transfer in the majority of cases is via an API between a WITSML Server (predominantly associated with gathering or aggregating data from the rigsite) and the WITSML Client component of a real-time enabled consuming application. Although most WITSML servers are able to work with a broad range of data objects, clients need only be configured to receive data from the objects commonly used by that application. Increasingly as bi directional flow of drilling data becomes more prevalent the need is developing for servers and some end use applications to act as both WITSML servers and WITSML clients.

Real-time solutions provide a critical decision support and collaboration platform that enable better decisions throughout the well construction and production lifecycle. Operators, drilling contractors, and service companies use these real-time capabilities to improve operations service quality, monitor efficiencies, understand formation geology, and enhance overall reservoir knowledge. Real-time operating centers are typically staffed with domain experts; they can provide surveillance to support less experienced wellsite personnel, provide advice and guidance, and support remotely performed operations. Data systems must acquire, process, aggregate, distribute, and present relevant real-time information quickly and accurately to ensure a rapid assessment of the current situation. However, their effectiveness depends on adequate real-time service quality and standardized data delivery. Many factors can contribute to either poor data quality or inconsistent, untimely data distribution. In addition to the most obvious technology limitations, such as available bandwidth, latency, or overall system performance, many nontechnical influences can also negatively affect performance. All too often, technology is relied upon to provide these solutions, and the human factor is taken for granted or ignored. These human factors can include failing to set pre-job expectations, setup and configuration, monitoring protocols, or communication protocols. Other human factors issues can include a lack of data standardization or established governance regarding the management of the data and its underlying quality. This paper presents a case history that describes how the collaborative effort between a major NOC and service company was used to substantially improve the value delivered from consistent real-time data and service quality. Specifically, this paper addresses the identification of the problem, root cause analysis, and specific remedies that were implemented, as well as clear real-time key performance indicators that could be used to measure and monitor improved performance.

Over the last decade, change management has had significant focus in Statoil, and we have implemented a large number of major changes to our operational work processes in order to improve decision support and drilling efficiency. Deployment of new technology and new processes utilizing real-time drilling data affect not only Statoil as operator, but also service companies, contractors, application vendors, and standardization committees. Implementing internal changes affecting day-to-day operations is in itself difficult. Implementing changes also affecting and changing other companies' processes and deliverables are even harder. Change management in Statoil have had many different approaches with regards to implementation and results. In hindsight we see that some of the transitions have been very successful, whilst others have been more challenging. We also see that the objectives and characteristics of the various transitions heavily influence the optimum change management process. Small differences in the approach can drastically influence the outcome. In this paper we will present some of our observations, learning's and experiences implementing new processes, new technology and new contracts within the domain of real-time drilling data. We will touch upon the following topics: Optimizing the collaboration: operators, service companies, contractors.Change management focus: Functionality vs. technology.Commercial technology vs. CustomizationsContract strategy The cases in this paper will illustrate each learning experience, both good and bad. Finally, we will present a summary of our experiences and recommendations, hoping that this may be of value to others working with change management processes within the exciting area of Intelligent Energy.

Data quality issues have for many decades been a problem for drilling data. To some extent, development of data transfer standards has helped out in achieving better data quality and data transport. In the early stages of WITSML, poor data quality was a concern and in this paper we will be looking at various steps that have been taken to improve data quality. Sensor technology has improved a lot in recent years with fieldbus options which allow for remote calibration and diagnostic. In addition calibration routines are streamlined and range checks can be implemented at point of acquisition. The data acquisition software now has some inbuilt quality control to addresses errors in manual data input. In addition we have developed software at the rig-site that will perform several data quality checks in the database. After acquisition, the data is converted and transferred to a central hosted WITSML 1.4.1.1 server. Here several applications will perform data quality assurance on the data, e.g. to check for data gaps. In addition the data flow is monitored 24/7 from an operation center before data is consumed by several applications. We have been working closely with one operator for several years to improve processes in WITSML data deliveries. To ensure there is an agreement of what data is expected to be delivered, this company has established electronic order forms that will be sent to us for quality check before the section starts. In addition this operator has developed a sophisticated data quality monitoring system that will produce KPI scores linked to the SLA. Some results from research in using statistics to uncover abnormal sensor response in acquired data will also be presented. Statistic will show how data quality is improving while the amount of data is acquired from one rig is increasing year by year.