Stochastic Drilling Forecasting via Monte Carlo Simulation

The patent replaces traditional deterministic mechanical estimation methods with a stochastic computational modeling system. The system uses probability distributions, Monte Carlo simulations, and statistical analysis to model drilling cost and time uncertainties, substituting simple calculation mechanisms with sophisticated computational algorithms that quantify variability and risk.

The patent introduces stochastic modeling software and computational algorithms as intermediaries between historical drilling data and forecast results. This intermediary layer processes raw historical data through probability distributions and simulation models to produce refined forecasts with quantified uncertainty ranges, acting as a bridge between simple data collection and accurate predictive output.

The patent segments the complex forecasting problem into distinct modular components: historical data collection modules, probability distribution fitting modules, Monte Carlo simulation modules, and result analysis modules. Each module handles a specific aspect of the forecasting process, allowing multiple factors to be considered independently and systematically while maintaining overall system manageability.

The patent transforms fixed deterministic parameters into variable stochastic parameters with associated probability distributions. Instead of using single fixed values for drilling time and cost, the system assigns probability distributions to multiple factors (rig type, well depth, location, geological conditions), allowing the model to capture the inherent variability and interrelationships among these factors.

The patent implements a tiered stochastic modeling approach where the full computational power is applied only when necessary. The system can perform rapid deterministic estimates for preliminary planning, then apply full stochastic modeling only for critical wells or high-stakes decisions, achieving high reliability where needed while minimizing unnecessary computational overhead for routine operations.

The patent performs preliminary data preparation and probability distribution fitting during the planning phase, storing pre-processed historical data and fitted distributions. When actual forecasting is needed, the system retrieves pre-prepared models and runs simulations more efficiently, having already done the computationally intensive data cleaning, normalization, and distribution fitting work in advance.