The dramatic drop in crude oil prices has put the oil and gas industry under tremendous pressure to cut costs and find new areas of improvement. While signs the market is starting to stabilize exist, oil and gas companies will still need to optimize operations to stay competitive. One of the best ways to reach this goal is through data analytics solutions.
Increase Drilling Uptime
Drilling operations have room for improvement. IDC Energy Insights reported that, while drilling costs represent nearly half of well expenditures, only 42 percent of drilling operations’ time is actually spent drilling. The majority of their time is spent on problems, rig movement, defects and waiting periods.
One way to improve production is by using data analytics that help operations do the following:
- Enable operators to correlate real-time downhole drilling data with production data of nearby wells to optimize drilling strategy. According to industry experts, this practice can help oil and gas companies increase production by 6 to 8 percent.
- Avoid potential problems by identifying deviations from signature profiles; for example, ensuring the downhole equipment and reservoir are protected when bringing wells online to avoid costly mistakes.
- Predict downhole tool failures and immediately determine which parameters may need adjusting by analyzing real-time data relative to past performance.
Expect the Unexpected
Wells have a type-curve that profiles expected production over their lifetime. Decline curve analysis (DCA) is a common graphical procedure used for understanding declining production rates and forecasting future performance. Over time, well production rates typically decrease from a loss of reservoir pressure or changing volumes of the produced fluids. The DCA concept involves fitting a line through the performance history then assuming this same trend will continue in future forms.
While helpful, these curve analysis trends can be made more precise through the use of analytics. For example, an analytics solution can quickly identify when flow profiles deviate from the historical or type-curves. This information can alert an operator of a potential problem that is about to occur.
Data analytics can improve a company’s bottom line by prolonging equipment life, extending maintenance windows and increasing asset availability.
ARC Advisory Group reported that, of all asset failures that occur, only 18 percent show any kind of perceptible pattern due to any increased use or age. By identifying changes in system behavior well before traditional operational alarms do, analytics software gives decision makers the time they need for analysis and corrective action.
For example, data mining and analytics can make a big difference in pressure testing blowout preventers (BOPs). According to the government specification 30 CFR 250.517 for tubing and wellhead equipment, pressure tests must be conducted every two weeks for every single drilling operation worldwide to ensure that BOPs can withhold a well control event.
Analytics software can track the actual component performance data and look at profiles to improve BOP system uptime, reduce unnecessary maintenance and help deliver better cost forecasting. It can provide information to onshore engineers for better decision making, decreasing downtime associated with accessing historical BOP data while optimizing maintenance and reducing unnecessary parts replacements.
The New, Affordable Approach to Analytics
One barrier to identifying and monitoring various interconnected elements in production cycles is cost. Having data scientists build models and simulations to determine how minor changes to certain areas of operations can yield significant productivity gains is expensive and time-consuming.
With low oil prices and tight profit margins, many oil and gas companies believe they cannot afford the investment in traditional analytics solutions.
Fortunately, there is a new method to uncovering areas for improvement that does not require expensive modeling and data scientists. This approach is based on pattern recognition within the enormous pool of data.
Sophisticated software can read trends to determine where similar patterns have occurred in the past. Moreover, this software can be installed and deployed in as little as two hours with just another couple of hours needed for training operators and other authorized users.
This analytics software can uncover hidden patterns in data to find an optimal sequence, which is repeatable. It can identify and send alerts about any deviations (in profile and duration) in both real time and also in post-run analysis.
For instance, these data mining techniques can be applied to a comprehensive data set to identify potential correlations between drilling activity and incremental rate of penetration (ROP). It can help calculate drilling performance in real time, under specific conditions and constraints.
Data mining techniques also can help with cementing operations. Cementing casing strings into drilled holes is a critical operation in well construction. History indicates that many of the cement jobs performed in the industry did not achieve quality metrics.
Yet the work is usually deemed acceptable because there was no means to analyze the quality until now. Today’s advanced software can perform that level of analysis in real time or immediately after as an evaluation technique.
Getting Ahead in a Tough Market
Today’s oil and gas companies face many challenges. To be successful, they must ensure all areas of operations are performing optimally. It is essential to have the right information at all levels of the organization in order to make the best decisions.
At the production unit level, operators need to know how equipment is performing in real time. This is where analytics could be used to improve uptime, efficiency and throughput.
At the facilities level, managers must be able to make educated decisions about procurement, production scheduling and shipping without having to spend lots of time and money on modeling and data scientists.
At the enterprise level, executives need real-time, accurate data to relate production to the larger business context and understand the impact of fluctuating costs, changing market conditions and asset performance.
Although crude oil prices may have reached a bottom, oil and gas producers are still under considerable pressure to optimize all areas of production. In the past, expensive data analytics solutions could be used to uncover hidden areas of improvement, but these solutions required data scientists and modeling to work effectively.
Now oil and gas companies have a quick, easy and affordable solution for optimizing performance through pattern-recognition software that can quickly sift through billions of time series data points to find instances where events have occurred.
By using this new approach to data analytics, oil and gas companies gain valuable insight into operations and systems behavior—often in just a few hours after installation—to discover new areas for improvement.