JMP® Visual Discovery for Oil and Gas
As the demand for hydrocarbon-based energy grows, oil and gas companies aim to become more efficient in the extraction of fuels from sources they have already discovered. They also seek new sources of oil and gas, and want to diversify the sources of supply by using different feedstocks. JMP provides visual analytics and exploratory data analysis to support these goals and the environmental aims of the industry — from upstream exploration and extraction operations to downstream refining operations.
JMP can help:
Increase production rates and lower production costs.
Minimize environmental impact.
Escalate time to drilling.
Reduce equipment failure rates.
Lower maintenance costs and equipment downtime.
Minimize service wait times for your customers.
Standardize analysis steps.
Create interactive visual reports to describe complex relationships to management.
Support Lean Six Sigma visually and intuitively.
Maximize Oil and Gas Recovery
The oil and gas industry has extracted 1 trillion barrels of oil and believes that 2 trillion barrels of oil still remain as “deep oil” in the oil fields. The industry uses deep-reading reservoir measurements (seismic, microseismic, electromagnetic, gravimetric, pressure interpretation) to fully monitor a reservoir throughout its lifetime and determine the efficacy and cost of extracting oil from these deep reservoirs.
Oil and gas companies can use JMP to help determine ways to increase recovery by analyzing reservoir measurements, including data from both conventional and unconventional sources of oil and gas. JMP analyses include various modeling techniques such as regression analysis and neural net, plus clustering, principal components and more.
The industry is committed to extracting and storing resources in an environmentally friendly manner. Companies are analyzing new alternatives such as using CO2 underground storage to mitigate greenhouse gas emissions; managing water in maturing oil and gas fields to reduce water use and avoid contamination; and reducing production footprints in environmentally sensitive areas.
JMP can be used to design experiments that help establish conditions that are optimal for increasing sustainability, to monitor the conditions over time and to analyze changes needed to maintain sustainability.
Discover and Tap New Sources
New sources in harsh conditions hold promise as sources for untapped hydrocarbon accumulations. Harsh geographic conditions such as arctic, ultra-deep and new continental shelf locations present challenges to exploration and extraction. Additionally, high-acid gas content increasingly found in oil and gas accumulations constitutes another harsh condition that operators must face and manage.
JMP can be used to access and analyze the myriad types of data needed to model and monitor environmental conditions along with hydrocarbon characteristics and hazards. JMP can also create a variety of efficient experimental designs for extracting information from simulation results with the least number of simulation runs.
Maximizing operating efficiency, minimizing well and plant downtime, managing wellhead imbalances, and developing and managing predictive maintenance programs are among the challenges facing the industry. In addition, predicting and preventing fatigue in the materials such as concrete and hydraulics used in extraction are critical tasks in an industry whose product is extracted under high pressure far below the earth’s surface. Damage to these systems can lead to uncontrolled loss of hydrocarbons.
JMP can be used for production modeling, statistical process control and reliability analysis.
This graph indicates that the failures observed to date for pumps A and B have occurred earlier than the failures for pumps C and D.
Escalate Time to Drilling
Reservoir simulators that integrate static geological information with dynamic engineering data to represent the flow of liquids in rock have been used extensively by the petroleum industry for planning and evaluating potential drilling sites. These simulations are very time consuming. JMP offers state-of-the-art experimental design methodologies to efficiently assess uncertainties by determining the minimum number of simulations needed to adequately characterize the field and production techniques. In addition to the classical experimental designs typically used, JMP’s Design of Experiments (DOE) platform also offers optimal designs (both D and I-optimal) for more flexibility in the face of real-world process constraints and modern space filling designs, which are used when complex simulations require better spread of data through the design space to capture local structure and process optima.
Mining historical data with JMP Partition Modeling helps determine the best predictors for experimental design and modeling.
JMP’s Prediction Profiler is used to interact with the model to see how changes in the control factors affect the calculated response value. In addition to confidence limits about the point estimate, the uncertainty about the prediction due to variability about the factor settings can also be visualized.