tenokonda

Analog Ranking

Systematic and robust referencing of reservoir analogues.  Ranking and evaluation of reservoirs based on a set of key geologic parameters. Estimation of expected recovery. Rapid decisions on investment allocation.

Time Series Forecasting

We employ state of the art Deep Learning techniques to provide time series forecasting for several horizons including daily or higher frequencies. Commodities, financials, and other type of time series can be predicted in a framework that is capable of combining text (e.g. news feed) and numbers (e.g. other input time series) 

Drilling Events Detection

Drilling is one of the most expensive and risky upstream activity. It is important to detect in advance events (e.g. stuck rig in mud, broken rig) in order to act fast and efficiently. We use Natural Language Processing combined with advanced Machine Learning techniques to detect in advance potential downtime. Also diagnose real time non-productive wells.

Decline Curve Calibration

Select a functional form or define your own, choose which parameters to calibrate to get instantly the resulting decline fitted curve. You can also choose different automation levels and run in batch mode for large datasets.

Equity Markets Sentiment Analysis

Social Media is growing in volume and will inevitably be part of trading sources for pricing and risk management. We have worked on using Artificial Intelligence to provide sentiment analysis on major stocks and indices.

Data Imputation

Missing data is part of almost every dataset in practice. In several cases we cannot just discard the missing data, instead need to impute it. Backtesting is an obvious example where data imputation has a lot of value. We employ Machine Learning technique to provide realistic and fast data imputation.

Value at Risk Backtesting

Backtesting can be computationally intensive and hard to set up. We provide flexible, efficient and scalable Value at Risk backtesting solutions for any asset class.

Portfolio Optimization

From classic Markowitz to powerful genetic algorithms handling multiple objectives and non linear constraints, our portfolio optimization solution is flexible and robust. You can also define custom portfolios and see how they perform against the efficient frontier returned by the optimizer.

Volatility Smile Calibration

Calibrate volatility smiles and surfaces using SABR, SVI or Heston models and their variations. Our framework allows for fast calibration on large historical data sets.

Scenario Engine

Our Scenario Engine solution allows you to generate historical and Monte Carlo scenarios based on your time series data. It provides a wide range of options in terms of returns calculation, covariance structure estimation and simulation methods. It also offers the possibility to use high dimensional Sobol random number generators improving the statistical properties of your simulations.