Petroleum engineers are always in a race to maximize the recovery factor out of naturally trapped hydrocarbon resources. Unconventional resources such as organic-rich shales have unlocked significant reserves attributed to the novel production technologies of lateral drilling assisted by hydraulic fracturing. Even though such techniques have enabled the exploitation of shales, the ultimate recovery remained fractional, a challenge to be answered through further improvement. Carbon dioxide injection in unconventional resources, which was initially implemented for coalbed methane, has been recently an active area of investigation for organic-rich shales. In this paper, we present a molecular modeling study of carbon dioxide injection in the organic matter of the shale matrix. We built the molecular model, consistent with the repeated organic matter characterization in the literature. Molecular dynamics (MD) protocol was developed to form a three-dimensional (3-D) configuration of kerogen, followed by Gibbs Monte Carlo simulation for the adsorption/desorption calculations, and self-diffusivity calculations through MD. The aim was to delineate the impact of carbon dioxide injection on the adsorption/desorption behavior coupled with its influence on the transport. Injection of carbon dioxide was found to shift the adsorption isotherm favoring the depletion of methane. The ultimate recovery raised from 54% (no injection of CO 2 ) up to 92% depending on the carbon dioxide concentration and its temperature. Moreover, the injection of carbon dioxide was found to have a minimal impact on the self-diffusivity of methane in kerogen bodies and their associated microcracks.

No abstract

No abstract

Warning signs of possible kick during drilling operation can either be primary (flow rate increase and pit gain) or secondary (drilling break, pump pressure decrease,). Drillers rely on pressure data at the surface to determine in-situ downhole conditions while drilling. The surface pressure reading is always available and accessible. However, understanding or interpretation of this data is often ambiguous. This study analyses significant kick symptoms in the wellbore annulus while under shut-in conditions. We have tied several observed annular flow patterns to the measured pressure gradient during water- air, and water-carbon dioxide complex flow. This is based on experiments in a 140-ft high flow loop, with a hydraulic diameter of approximately 3 in. The experiments were carried out under static conditions to simulate kick occurrence when the drilling fluid is not flowing, typically the wellbore is shut-in. We used an Artificial Neural Network (ANN) and K-Means clustering approach for kick prognosis. We trained these Machine learning models to detect kick symptoms from pressure response and gas evolution data collected between the kick occurrence and the Wellhead. The Artificial Neural Network (ANN) approach was relatively fast with a negligible difference in accuracy when compared for air influx and carbon dioxide influx for kick prognosis. The ANN resulted in an accuracy of about 90% and 93% for air-based kick prognosis. While the accuracy was 92% and 94% for carbon dioxide-based influx. With K-mean clustering, the Silhouette score were 0.5 and 0.6 for the air and carbon dioxide influx respectively. The estimation of the influx size and type is strongly a function of the duration of kick and bottom hole underbalanced pressure. Based on visual analysis, pit gain, and pressure signals, the quantity of the mass influx significantly controls the flow pattern, pressure losses, and pressure gradient as the kick migrates to the surface. The resulting turbulent flow after the initial kick (After Taylor bubble flow) varied with duration of kick, average liquid flow rate, influx type, and drilling scenario. We have tied the surface pressure readings to the flow regimes to better visualize well control approach while drilling. This is based on relating the significant kick symptoms we observed to the pressure readings at multiple locations and time, then training the Deep learning models based on this data. Although computationally demanding, the Deep-Learning model can use the surface pressure data to relay annular flow patterns while drilling. This work provides an alternative and relatively accessible primary kick detection tool for drillers based on measured pressure responses at the surface.