“Our mission is to empower you to understand, predict, and enhance
your product and process performance.”
Let Us Explain...
Our team is comprised of Engineers. We have been Product Engineers, Process Engineers, and Capital Project Engineers and have walked many miles in your shoes. We’ve stood in front of control panels at 3 AM wondering how to bring reject production back into specification. Since founding our company back in 1992, we’ve had the privilege of working with many other engineers like you throughout the world to solve difficult challenges and bring great benefits.
Example Success Stories
Predicting Oil, Gas, Water, Condensate, ...
We extract production data while on test from your data historian (or collect it from the DCS into our historian), model production vs. well conditions, and then put the model online in real-time. Asset managers and reservoir engineers can see production from each well live and can reconcile production vs. production separators.
We have been implementing this solution around the world for almost 25 years. We call it “Virtual Metering”.
Single Well Optimization: Up to 500% Gains (?!)
By modeling the well conditions (pressures and temperatures) vs. production on test, then “inverting” those models to determine controllable factors, such as lift gas assist and choke, we can optimize the production of wells. We’ve seen a rate increase as much as 5x.
Multi-Well Optimization: 20% Gains Fieldwide
Instead of optimizing each well individually, which leads to local not global gains across an oil field, we optimize entire fields, or even multiple fields simultaneously, directing just the right or maximal production to central processing facilities. We have seen up to 20% higher and more consistent production.
Optimal Assembly Operations
The customer was making a sophisticated product from a dozen subassemblies and when done, they adjusted “tuning resistors” to dial in the product. Even then, they were getting a 60% reject rate. We helped them model 57 final product performance vs. subassembly QC properties and then “virtually assembled” combinations of subassemblies such that all parts were used. The AI even dialed in the resistors and told them the settings to use. The result was a 100% pass rate, a great success. The customer got an award.
Occasional Reject Product and No One Knows Why
A nylon manufacturer suffered from occasional product failures and had no idea why. We came in, discussed the products and processes, and then worked with them to obtain data for good and bad products. The discriminating model pointed directly to the cause. The customer changed a procedure, enabling a half-billion-dollar scale-up project to proceed.
Estimating Product Properties
In many cases, there is no theoretical basis for the “quality” of a product. There is no equation for the “wet burst strength” of a paper towel. But we can model the raw materials characteristics and process conditions in manufacturing and build a model using QC lab results to estimate product performance in real-time as the product is made, then use those models to control product performance.