The Effect of Capillary Condensation on the Geomechanical and Acoustic Properties of Tight Formations: An Experimental Study

Capillary condensation is the condensation of the gas inside nano-pore space at a pressure lower than the bulk dew point pressure as the result of multilayer adsorption due to the high capillary pressure inside the small pore throat of unconventional rocks. The condensation of liquid in nano-pore space of rock changes its mechanical and acoustic properties. Acoustic properties variation due to capillary condensation provides us a tool to monitor phase change in reservoir as a result of nano-confinement as well as mapping the area where phase change occurs as well as characterize pore size distribution. This is particularly important for tight formations where confinement has a strong effect on phase behavior that is challenging to measure experimentally. Theoretical studies have examined the effects of capillary condensation; however, these findings have not been verified experimentally. The main objective of this study is to experimentally investigate the effect of capillary condensation on the mechanical and acoustic properties of shale samples. The mechanical and acoustic characterization of the samples was carried out experimentally using a state-of-the-art tri-axial facility at the Colorado School of Mines. The experimental set-up is capable of the simultaneous acquisition of coupled stress, strain, resistivity, acoustic and flow data. Carbon dioxide was used as the pore pressure fluid in these experiments. After a comprehensive characterization of shale samples, experiments were conducted by increasing the pore pressure until condensation occurs while monitoring the mechanical and acoustic properties of the sample to quantify the effect of capillary condensation on the mechanical and acoustic properties of the sample. Experimental data show a 5% increase in Young's Modulus as condensation occurs. This increase is attributed to the increase in pore stiffness as condensation occurs reinforcing the grain contact. An initial decrease in compressional velocity was observed as pore pressure increases before condensation occurs which is attributed to the expansion of the pore volume when pore pressure increases. After this initial decrease, compressional velocity slightly increases at a pressure around 750 - 800 psi which is close to the condensation pressure. We also observed a noticeable increase in shear velocity when capillary condensation occurs, this could be due to the immobility of the condensed liquid phase at the pore throats. The changes of geomechanical and acoustic signatures were observed at around 750 - 800 psi at 27°C, which is the dew point pressure of the fluid in the nano-pore space of the sample at this temperature. While the unconfined bulk dew point pressure of carbon dioxide at the same temperature is 977 psi. Hence, this study marks the first measurement of the dew point of fluid in nano-pore space and potentially leads to the construction of the phase envelope of fluid under confinement.

A Comparative Analysis of the Prediction of Gas Condensate Dew Point Pressure Using Advanced Machine Learning Algorithms

Dew point pressure (DPP) is a key variable that may be needed to predict the condensate to gas ratio behavior of a reservoir along with some production/completion related issues and calibrate/constrain the EOS models for integrated modeling. However, DPP is a challenging property in terms of its predictability. Recognizing the complexities, we present a state-of-the-art method for DPP prediction using advanced machine learning (ML) techniques. We compare the outcomes of our methodology with that of published empirical correlation-based approaches on two datasets with small sizes and different inputs. Our ML method noticeably outperforms the correlation-based predictors while also showing its flexibility and robustness even with small training datasets provided various classes of fluids are represented within the datasets. We have collected the condensate PVT data from public domain resources and GeoMark RFDBASE containing dew point pressure (the target variable), and the compositional data (mole percentage of each component), temperature, molecular weight (MW), MW and specific gravity (SG) of heptane plus as input variables. Using domain knowledge, before embarking the study, we have extensively checked the measurement quality and the outcomes using statistical techniques. We then apply advanced ML techniques to train predictive models with cross-validation to avoid overfitting the models to the small datasets. We compare our models against the best published DDP predictors with empirical correlation-based techniques. For fair comparisons, the correlation-based predictors are also trained using the underlying datasets. In order to improve the outcomes and using the generalized input data, pseudo-critical properties and artificial proxy features are also employed.

Condensate banking removal: study on ultrasonic amplitude effect

Hydrocarbons in a gas condensate reservoir consist of a wide variety of molecules which will react varyingly with the change of pressure inside the reservoir and wellbore. The presence of heavier ended hydrocarbons such as C5 and above, condensate banking will occur as pressure depletes. Pressure drop below dew point pressure causes condensate buildup which will give a negative impact in the productivity index of a gas condensate reservoir. Gas condensate reservoirs experience liquid drop out when pressure depletion reaches below dew point pressure. This occurrence will eventually cause condensate banking over time of production where condensate builds up in pore spaces of near-wellbore formations. Due to increase in condensate saturation, gas mobility is reduced and causes reduction of recoverable hydrocarbons. Instead of remediating production loss by using unsustainable recovery techniques, sonication is used to assist the natural flow of a gas condensate reservoir. This study aims to evaluate the effects of various ultrasonic amplitudes on condensate removal in a heterogenous glass pack in flowing conditions with varying exposure durations. Experiments were conducted by using n-Decane and a glass pack to represent condensate banking and near-wellbore area. Carbon dioxide was flowed through the pack to represent flowing gas from the reservoir after sonication of 10%, 50% and 100% amplitudes (20 kHz and 20 Watts). Analysis of results shows recovery of up to 17.36% and an areal sweep efficiency increase in 24.33% after sonication of 100% amplitude for 120 min due to reduction in viscosity. It was concluded that sweeping efficiency and reciprocal mobility ratio are increased with sonication of 100% amplitude for 120 min. This indicates that mobility of n-Decane is improved after sonication to allow higher hydrocarbon liquid production. Insights into the aspects of the mechanical wave are expected to contribute to a better understanding of tuning the sonic wave, to deliver remarkable results in a closed solid and fluid system. This form of IOR has not only proved to be an effective method to increase productivity in gas condensate wells, but it is also an environmentally sustainable and cost-effective method.

Retrograde gas condensate reservoirs: reliable estimation of dew point pressure by the hybrid neuro-fuzzy connectionist paradigm

The dew point pressure (DPP) is a crucial thermodynamic property for gas reservoir performance evaluation, gas/condensate characterization, reservoir development and management, and downstream facility design. However, dew point pressure measurement is an expensive and time-consuming task; its estimation using the thermodynamic approaches has convergency problems, and available empirical correlations often provide high uncertainty levels. In this paper, the hybrid neuro-fuzzy connectionist paradigm is developed using 390 literature measurements. The adaptive neuro-fuzzy inference system (ANFIS) topology, including the training algorithm and cluster radius (radii), was determined by combining trial-and-error and statistical analyses. The hybrid optimization algorithm and radii=0.675 are distinguished as the best characteristics for the ANFIS model. A high value of observed R2 = 0.97948 confirms the excellent performance of the designed approach for calculating the DPP of retrograde gas condensate reservoirs. Furthermore, visual inspections and statistical indices are employed to compare the ANFIS reliability and available empirical correlations. The results showed that the ANFIS model is more accurate than the well-known empirical correlations and previous intelligent paradigms in the literature. The designed ANFIS model, the best empirical correlation, and the most accurate intelligent paradigm in the literature present the absolute average relative deviation (AARD) of 1.60%, 11.25%, 2.10%, and, respectively.