Converting Tank Level Sensor Data Into Daily Cashflow using AI & ML models

Converting Tank Level Sensor Data Into Daily Cashflow using AI & ML models
SOTA X analyzes tank levels for oil and natural gas companies. SOTA X uses AI-powered OCR to read and compare the real-time data of tank levels. SOTA X can examine your tank level information utilizing AI and ML calculations to assess your 24-hour production and sales. It assists you with predicting your losses and gains in operations.
Oil companies are compensated for the production of the oil and charged for disposing of the water from the crude oil, power, compression, etc. SOTA X tracks the amount of oil and gas purchased along with the charges for water removal. Lower operational processes are very crucial for managing the cash flow.
How does SOTA X help our clients?
SOTA X is equipped with a predictive analytics model that helps to analyze and track the number of barrels sold based on several sets of sensor data, also it helps in further analysis of tank levels, meters, and ML-based rate of change calculations, etc. such organization reached us as of late to comprehend the reason why they have come up short on a significant degree of oil barrels. When we went over this case and researched, our examination mirrored that the organization had sold 129,598 barrels however they were just paid for 128,396 barrels. Furthermore, if we calculate the cash flow at stake (oil being $75/barrel), payment of an amount of $90,150 was missing. It was truly a disclosure when we figured out the purchaser had not represented the numbers appropriately for 9 days. Our client (the company) had transparent, defensible and time-stamped sensor data, for which they asked the purchasers to provide proof and paperwork for the same. The purchaser furnished the data except for those 9 days. It was found out that their pipeline & tank measurement systems were not functional for which they entered the data manually. After this disclosure, the buyer had to remunerate the remainder of the amount that they didn't.