Deep platform is a domain know how, machine learning and data analytics platform that enables oil and gas companies to make informed decisions on production management, asset performance and operations optimization.
DeepsinAI works with learning algorithms and artificial intelligence to significantly reduce operating costs by optimizing production, optimizing well operations, and reducing time to decision activity to increase revenue or reduce cost.
DeepsinAI is a cognitive automation solution used by the oil & gas industry to make operations more efficient.
DeepsinAI works with learning algorithms and artificial intelligence to provide production operators with real-time insights into their daily activities, as well as historical data on past operations.
Our dashboard is designed to provide real time data in a glance. Create dashboards to keep an eye on your production and other important information regarding your company. Customize the dashboard according to your needs, so you can have all the necessary information in front of your eyes. Make faster decisions with our dashboard and increase productivity and quality.
The Well Matrix is a powerful tool to help you monitor your wells. You can use it to keep an eye on critical wells, or track the completion of workovers and shut-ins. You can even set alarms for increased or decreased production, allowing you to make swift decisions on the health of your well. This will give you a leg up on your competition in managing critical wells.
Deep Platform Modules
DECO 2 Module
The DECO 2 module is an excellent tool to calculate your GHG emissions. Accessing GHG emissions data and calculating inventories is not a simple task. The DECO 2 Module empowers you to aggregate, sort, and filter emissions data. Easily compare emissions sources and resource consumption with the Emissions Factor Database (EFD) populated with global estimates for all major emission sources. This Module from Deep Platform assists you in understanding the dynamics of your company’s emissions, quantify the impact of emission reductions on costs and revenues, and visualize your de-carbonization pathway to zero net CO 2 emissions and test assumptions up front. Emissions factor database includingAPI and IPCC.
The DECO 2 Module empowers you to aggregate, sort, and filter emissions data.
With DECO 2 Module, you will be able to collect the data from all emission sources such as stationary
combustion, flaring, venting and sort them into Scope 1 and Scope 2 emissions.
EASY DCA Module
Decline Curve Analysis (DCA) is the standard industry approach to forecast the production of oil and gas wells. The process can be extremely time consuming when hundreds or thousands of wells have to be reviewed to create a field forecast. The patented numerical engine and machine learning-based type curving substantially reduces the time for estimating ultimate recovery while performing unbiased and
systematic analysis to any number of wells.
This is a powerful ML driven module for automatic diagnostic of SRP pump, solutions when well is stopped, analytics, fast, precise, data driven and based on domain knowledge. AIR tool can identify the problem when the well is out of service and the pump is not working properly. Also provides all information about well and utilize those information for automated decision-making solutions.
AIRFLOW Module is a state of art machine learning model in combination with our production data to predict well flow rate. It should be used in combination with stationary dynamometers on wells that are currently producing in artificial lift system called SRP. It will predict future rates when the well goes past that point.
With this module you can predict the future well flow rates, while you are running a production.
ALS Selection Module
The selection of artificial lift method is an expensive and complex process that involves several iterations of design parameters. The human-curated selection process requires the decision making with unbiased, repeatable and reliable. This project aims to provide a solution for improving the human decision by using supervised machine learning which incorporate past performance and lesson learnt from previous wells.
To achieve a better selection of artificial lift method, we utilize the supervised machine learning methods and well history data.