Content Tagged: "production forecasting"

Develop a strategic scenario and budget for implementing a reservoir development scheme and producing actual reserves. Upon completion of this module, the participant should be able to use reservoir studies to develop a resource base (drilling plans, future improved recovery, information acquisition, application of new technologies, facilities and infrastructure)and evaluate reservoir exploitation schemes through the use of corporate management indicators.

This SPE Live covers a methodology for addressing uncertainty in the energy industry's production forecasting, model updating, and decision-making pro

The oil and gas industry uses static and dynamic reservoir models to assess volumetrics and to help evaluate development options via production forecasts. The models are routinely generated using sophisticated software. Elegant geological models are generated without a full understanding the limitations imposed by the data or the underlying stochastic algorithms. Key issues facing reservoir modelers that have been evaluated include use of reasonable semivariogram model parameters (a measure of heterogeneity), model grid size, and model complexity. However, reservoir forecasts tend to be optimistic – a statement not provable with data in the public domain. Yet, conversations at technical meetings, the lack of industry publications highlighting actual forecast accuracy, the development of more detailed reservoir models (presumably to yield better forecasts), all suggest that the industry could improve its reservoir performance forecast accuracy. For example, dynamic models that use larger grid cells yield optimistic forecasts for some recovery processes as compared to forecasts obtained from models built with smaller grid sizes. Also, the use of stochastic earth models and well placement optimization workflows will likely yield optimistic forecasts. Overall, the impact of cell size, model parameters, inadequate use of analog data, and poorly constrained well location optimization may increase forecast optimism by 5-10 recovery factor units or more. Knowing what workflow aspects may contribute to forecast optimism should enable the industry to generate more reliable forecasts and make better use of capital. Presented by W. Scott Meddaugh.

Decline curve analysis methods are the most widely used methods of performance forecasting in the petroleum industry. However, when these techniques are applied to production data from unconventional reservoirs they yield model parameters that result in infinite (nonphysical) values of reserves. Because these methods were empirically derived the model parameters are not functions of reservoir/well properties. Therefore detailed numerical flow simulation is usually required to obtain accurate rate and expected ultimate recovery (EUR) forecast. But this approach is time consuming and the inputs in to the simulator are highly uncertain. This renders it impractical for use in integrated asset models or field development optimization studies. The main objective of this study is to develop new and “simple" models to mitigate some of these limitations.

Modern and efficient reservoir management is imperative, given the ever-increasing demand for oil. Making the right decision on reservoir development utilizing all available data in a timely manner is the key to a successful operation. For mature reservoirs, this requires high-quality uncertainty assessment of long-term performance forecast estimations. One critical and difficult component of the total uncertainty in forecasting is the one that stems from the implicit uncertainty in the geological and reservoir simulation models. In fact, regardless of the amount of reservoir data that we collect, there is no way to define the reservoir model uniquely. This reality suggests that we use an integrated probabilistic framework and incorporate production data into the reservoir model to reduce the associated uncertainty in reservoir characterization and performance forecasting. The technical challenge is in obtaining a probabilistic description of the reservoir models. For mature reservoirs, this implies finding not one, but a large number of reservoir models that are consistent not only with the geological data but also with the production data. Applying smart sampling techniques combined with Monte Carlo simulation within a probabilistic framework, and utilizing available high-performance computing resources, it is feasible to find multiple solutions to the history matching problem. These solutions, in turn, can be used to estimate uncertainty for making good-quality reservoir management decisions in a realistic time frame. This presentation demonstrates the practicality of an approach to solve this critical problem using a real field example. Presented by Jorge Landa

The industry has devoted a great deal of attention in recent years to production decline models that might serve as alternatives to the Arps hyperbolic decline model for hydraulically fractured wells with long-duration transient flow. These efforts have arisen because the Arps model was developed for reservoirs in the boundary-dominated flow regime, whereas much onshore development in North America has focused on ultra-low permeability reservoirs, such as shale, which may remain in the transient flow regime for many months or years.