forecasting

This article presents a deep-learning approach, the long short-term memory network, for adaptive hydrocarbon production forecasting that takes historical operational and production information as input sequences to predict oil production as a function of operational plans.

Our shifting energy landscape requires a new way to measure the amount of energy that can be extracted from any given source against the energy required to produce and distribute it.

The authors discuss the development of a deep-learning model to identify errors in simulation-based performance prediction in unconventional reservoirs.

The authors of this paper propose a novel approach to data-driven modeling for transient production of oil wells.

The complete paper builds on existing tools in the literature to quantify the effect of changing well spacing on well productivity for a given completion design, using a new, simple, intuitive empirical equation.

This paper describes a development plan for an oil field discovered in a remote offshore environment in the Niger Delta that uses a probabilistic approach to estimate the STOOIP using low, mid, and high cases.

The paper provides insights on diffusion in organic matter to correct a primary source of underestimation of gas production in shale-gas models.

This paper presents a mathematical analysis of how incorrect estimates of initial reservoir pressure may affect rate-transient analysis in ultralow-permeability reservoirs.