Generative AI-Driven Forecasting of Oil and Water Production from Oilfield Sites with Multiple Wells

Core Concepts

Leveraging generative AI techniques, including TimeGrad and Informer models, to effectively forecast time series of oil and water production across multiple oilfield sites, capturing uncertainties and making precise predictions to inform decision-making processes.

Abstract

The article presents a novel approach to forecasting oil and water production from oilfield sites with multiple wells, using generative AI techniques. The key highlights are:

1. The oil rate forecasting problem is viewed as a time series forecasting challenge due to the temporal nature of the data, with challenges arising from incomplete understanding of multiphase flow physics, spatial heterogeneity in rock properties, and noisy fluctuations in non-stationary time series data.

2. The authors employ two generative AI methods - TimeGrad and Informer - to tackle the challenges in oil rate forecasting. TimeGrad combines a denoising diffusion probabilistic model (DDPM) with a recurrent neural network (RNN), while Informer utilizes a variant of the transformer architecture with sparse probability self-attention.

3. The models are trained and evaluated on data from four oilfield sites spanning four decades, forecasting oil and water production. The Informer model demonstrates superior performance compared to TimeGrad, accurately capturing the fluctuations in oil production across all sites.

4. The probabilistic nature of the models allows them to forecast the evolution of the probability density function (PDF) of oil production, enabling integration of uncertainty and variability in the properties and processes of both subsurface and surface components of an oilfield.

5. The authors highlight the potential of the generative AI-based forecasting framework for applications beyond oil production, such as the study of earthquakes induced by fluid extraction and injection in underground reservoirs.

Stats

"Forecasting oil and gas production volumes from wells in an oilfield is a classic petroleum engineering problem that has been extensively studied over the years."
"Precision in forecasting the produced fluid (oil, gas, and water) volumes plays an important role in determining the accuracy of economic projections, net present value calculation, hydrocarbon reserves estimation, number of wells to be drilled, construction and maintenance of fluid processing facilities, and oil price fluctuations within the petroleum industry."

Quotes

"Leveraging generative AI techniques, we model time series forecasting of oil and water productions across four multi-well sites spanning four decades."
"The novelty of these methods lies in their capability to track the time evolution of the forecast variable's PDF. This enables us to integrate uncertainty and variability in the properties and processes of both subsurface and surface components of an oilfield."

Key Insights Distilled From

Generative AI-driven forecasting of oil production

by Yash Gandhi,... at arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16482.pdf

Deeper Inquiries

How can the generative AI-based forecasting framework be extended to model the coupling between fluid flow and geomechanical processes, such as fault activation and induced seismicity, in underground reservoirs?

The generative AI-based forecasting framework, particularly through models like TimeGrad and Informer, can be extended to incorporate the coupling between fluid flow and geomechanical processes by integrating additional data sources and modeling techniques that capture the interactions between these domains. This can be achieved through the following approaches:

Multiphysics Modeling: By integrating multiphysics models that account for both fluid dynamics and geomechanical behavior, the framework can simulate how changes in fluid pressure and flow rates influence stress distributions in the reservoir. This can involve coupling the generative AI models with finite element or finite difference methods that solve the governing equations of fluid flow and geomechanics.

Data Fusion: Incorporating diverse datasets, such as seismic data, pressure measurements, and fluid production rates, can enhance the model's ability to capture the complex interactions between fluid flow and fault activation. Machine learning techniques can be employed to analyze these datasets and identify patterns that indicate potential seismic events related to fluid extraction or injection.

Probabilistic Framework: The probabilistic nature of generative AI models allows for the estimation of uncertainty in predictions. By modeling the probability density functions (PDFs) of both fluid production and geomechanical responses, the framework can provide insights into the likelihood of fault activation and induced seismicity under various operational scenarios.

Real-time Monitoring and Feedback: Implementing real-time monitoring systems that feed data back into the generative AI models can help refine predictions and adapt operations dynamically. This feedback loop can be crucial for managing risks associated with induced seismicity, allowing operators to adjust fluid injection or extraction rates based on the model's forecasts.

Scenario Analysis: The framework can be used to conduct scenario analyses that explore the impacts of different operational strategies on both fluid production and geomechanical stability. By simulating various conditions, operators can identify optimal strategies that minimize the risk of induced seismicity while maximizing production efficiency.

What are the potential limitations and challenges in applying the TimeGrad and Informer models to forecasting production from unconventional reservoirs, where the underlying physics and data characteristics may differ significantly from conventional reservoirs?

Applying TimeGrad and Informer models to forecasting production from unconventional reservoirs presents several limitations and challenges:

Data Scarcity and Quality: Unconventional reservoirs often have limited historical production data due to their relatively recent development. The quality of available data may also be inconsistent, leading to challenges in training robust models. The generative AI models require substantial amounts of high-quality data to learn effectively, and data gaps can hinder their performance.

Complex Flow Dynamics: The physics governing fluid flow in unconventional reservoirs, such as shale gas or tight oil formations, is often more complex than in conventional reservoirs. Factors like microfractures, varying permeability, and complex multiphase flow behavior can introduce significant nonlinearity and variability in the data, making it difficult for the models to capture these dynamics accurately.

Parameter Sensitivity: The performance of TimeGrad and Informer models can be sensitive to hyperparameters and model architecture choices. In unconventional reservoirs, where the underlying physics may not be well understood, selecting appropriate parameters can be challenging and may require extensive experimentation.

Multiscale Interactions: Unconventional reservoirs often exhibit multiscale interactions between geological features and fluid flow. The generative AI models may struggle to capture these interactions effectively, particularly if the training data does not encompass the full range of spatial and temporal scales involved.

Uncertainty Quantification: While the generative AI models provide probabilistic forecasts, quantifying uncertainty in unconventional reservoirs can be particularly challenging due to the complex interplay of geological, operational, and environmental factors. Accurately representing this uncertainty is crucial for informed decision-making but may require additional modeling efforts.

Integration with Existing Models: Integrating generative AI models with existing physics-based reservoir simulation models can be complex. Ensuring that the AI models complement rather than contradict established physical principles is essential for maintaining credibility and reliability in forecasts.

How can the insights gained from the probabilistic forecasts of oil and water production be leveraged to optimize field-scale operations, such as well placement, production scheduling, and fluid processing facility design?

The insights gained from probabilistic forecasts of oil and water production can significantly enhance field-scale operations through the following strategies:

Well Placement Optimization: By analyzing the probabilistic forecasts, operators can identify optimal locations for new wells based on predicted production rates and water cut. This data-driven approach allows for strategic placement that maximizes resource extraction while minimizing interference between wells, ultimately improving overall field productivity.

Production Scheduling: The probabilistic nature of the forecasts enables operators to develop more effective production schedules. By understanding the expected fluctuations in oil and water production, operators can adjust extraction rates to align with peak production periods, thereby optimizing resource utilization and enhancing economic returns.

Fluid Processing Facility Design: Insights from the forecasts can inform the design and capacity planning of fluid processing facilities. By anticipating variations in production rates and water content, operators can ensure that processing facilities are adequately sized and equipped to handle expected volumes, reducing bottlenecks and operational inefficiencies.

Risk Management: The probabilistic forecasts provide a framework for assessing risks associated with production variability. Operators can develop contingency plans based on different scenarios, allowing for proactive management of potential challenges such as equipment failures, supply chain disruptions, or unexpected changes in production rates.

Enhanced Decision-Making: The integration of probabilistic forecasts into decision-making processes allows for a more nuanced understanding of the uncertainties involved in oil and water production. This can lead to more informed decisions regarding investments, operational adjustments, and resource allocation, ultimately enhancing the overall efficiency and profitability of field operations.

Feedback Loop for Continuous Improvement: By continuously monitoring actual production against probabilistic forecasts, operators can refine their models and improve future predictions. This feedback loop fosters a culture of continuous improvement, enabling operators to adapt to changing conditions and optimize operations over time.

In summary, leveraging insights from probabilistic forecasts can lead to more strategic and efficient field-scale operations, ultimately enhancing the economic viability of oil and water production in complex reservoir environments.