Shale brittleness is one of the most important parameters to assess how the shale behaves upon subjecting to applied stress and evaluate the hydraulic fracturing treatment. Presence of lamination is a common feature in organic-rich shales which significantly create anisotropy in elastic properties and rock brittleness due to the platy minerals such as clays that have the tendency to be aligned in parallel orientation during burial and the digenesis process. Characterization of anisotropy and the understanding the controlling factors on the reservoir rock elastic properties, rock strength and rock brittleness are crucial for successful production and development of shales. The objective of this paper is to extend the previous discussion by (Ibrahim et al 2019), in which an integrated approach has been developed for evaluating the shale fracability, to explain the influences of shale lamination and emphasize on the effects of anisotropic in elastic properties on brittleness of organic-rich shales to better demonstrate the process of screening hydraulic fracturing candidate intervals and improve the hydraulic fracturing design which can eventually improve the production forecast. In this paper, we propose the vertical transverse isotropic (VTI) modeling to investigate the effect of shale lamination and anisotropy on rock elastic properties, tensile failure and wave velocity normal to bedding plane, which differ than they are when parallel to bedding plane. Throughout this study, it is observed that there is a remarkable effect of anisotropy parameters on rock elastic properties and tensile failure. This method help obtain more accurte brittleness index and give precise guide to optimize perforation depths choice and hydraulic fracturing design that can result in optimized hydrocarbon productivity.
Zhu, Ziming (Colorado School of Mines) | Fang, Chao (Virginia Polytechnic Institute and State University) | Qiao, Rui (Virginia Polytechnic Institute and State University) | Yin, Xiaolong (Colorado School of Mines) | Ozkan, Erdal (Colorado School of Mines)
In nanoporous rocks, potential size/mobility exclusion and fluid-rock interactions in nano-sized pores and pore throats can turn the rock into a semi-permeable membrane, blocking or hindering the passage of certain molecules while allowing other molecules to pass freely. In this work, we conducted several experiments to investigate whether CO2 can mitigate the sieving effect on the hydrocarbon molecules flowing through Niobrara samples. Molecular dynamics simulations of adsorption equilibrium with and without CO2 were performed to help understand the trends observed in the experiments. The procedure of the experiments includes pumping of liquid binary hydrocarbon mixtures (C10 C17) of known compositions into Niobrara samples, collecting of the effluents from the samples, and analysis of the compositions of the effluents. A specialized experimental setup that uses an in-line filter as a mini-core holder was built for this investigation. Niobrara samples were cored and machined into 0.5-inch diameter and 0.7-inch length mini-cores. Hydrocarbon mixtures were injected into the mini-cores and effluents were collected periodically and analyzed using gas chromatography (GC). After observing the membrane behavior of the mini-cores, CO2 huff-n-puff was performed at 600 psi, a pressure much lower than the miscibility pressure. CO2 was injected from the production side to soak the sample for a period, then the flow of the mixture was resumed and effluents were analyzed using GC. Experimental results show that CO2 huff-n-puff in several experiments noticeably mitigated the sieving of heavier component (C17). The observed increase in the fraction of C17 in the produced fluid can be either temporary or lasting. In most experiments, temporary increases in flow rates were also observed. Molecular dynamics simulation results suggest that, for a calcite surface in equilibrium with a binary mixture of C10 and C17, more C17 molecules adsorb on the carbonate surface than the C10 molecules. Once CO2 molecules are added to the system, CO2 displaces C10 and C17 from calcite. The experimentally observed increase in the fraction of C17 thus can be attributed to the release of adsorbed C17. This study suggests that surface effects play a significant role in affecting flows and compositions of fluids in tight formations. In unconventional oil reservoirs, observed enhanced recovery from CO2 huff-n-puff could be partly attributed to surface effects in addition to the recognized gas-liquid interaction mechanisms.
Eustes III, Alfred W. (Colorado School of Mines) | McKenna, Kirtland I. (Colorado School of Mines) | Zody, Zach J. (Colorado School of Mines) | Healy, Carl (Colorado School of Mines) | Lang, Camden (XTO) | Joshi, Deep (Colorado School of Mines) | Yow, Stephen (Chevron) | McGowen, Kyle (Shell)
Drilling education must evolve continuously to keep up with the changes in the drilling industry. Part of that evolution includes the addition of data analytics in drilling operations. In addition, having a "hands on" experience of actual drilling operations is an important part of the drilling engineering educational process. At the Colorado School of Mines, both goals are achieved using a new coring rig equipped with a high-frequency data acquisition system.
A Sandvik DE 130 Diamond Coring Rig was acquired by the school through a grant from Apache Corporation that has proven to be an excellent analog to full-scale petroleum rigs. It has all drilling subsystems such as rotary, hoisting, power, and circulation. A data acquisition system has been added that tracks accelerations as well as various drilling operational parameters. During experiments, each student has an opportunity to operate the driller's controls and experience the complexities associated with drilling operations including the occasional error. The retrieved core helps the student correlate the formation with drilling data.
The inclusion of the drilling experience in the curriculum has benefited the students in several aspects. This experience has helped students visualize drilling operations and understand complexities and challenges associated with drilling. During the drilling operations, if any problems arise, the students have a chance to troubleshoot those problems in real-time and apply their theoretical knowledge. Operational safety and stop work authority are also a focus and demonstrated by students. This is likely to be the first experience most students have with high-frequency drilling data analysis. Monitoring, collecting, and handling real high-volume data gives a first glimpse into the complexities of data analytics. Noisy realtime data and errors are real and observed by the students. They also learn to handle and analyze high- frequency drilling data identifying normal trends and abnormalities. This coring rig has enhanced the drilling engineering education and data analysis skills of our students.
This work outlines a novel teaching methodology that combines the practical understanding of drilling and the application of data analytics. Getting out to the field and actually drilling rock has enhanced our drilling curriculum to align it with the latest industry practice and to educate future drilling engineers.
The challenges related to liquid loading have been observed during flow-back after hydraulic fracturing, as well as during the production phase, and are further aggravated with the high inclination angles found in deviated wellbores. An experimental study was carried out to investigate the onset of liquid loading in a 6-inch production casing at various inclination angles. A unified mechanism model for the onset of liquid loading is developed for a large-diameter production casing.
The experimental setup includes a 6-inch acrylic test section which can be inclined from 0° to 90°. The study involves two-phase air-water flow in low liquid loading conditions to simulate a gas well. A dye- injection-system was used to detect the onset of liquid film reversal.
The experimental data demonstrates that the major factor that induces liquid accumulation is the liquid film reversal at pipe bottom. The critical gas velocity associated with the onset of liquid film reversal shows a strong function with the inclination angle and liquid flow rate in the current experimental study. Comparison with previous experimental data reveals that it also depends on the gas density and pipe diameter, i.e. it decreases with increasing gas density and increases when pipe diameter increases. Comprehensive model evaluation was conducted in the current study, showing a large discrepancy for inclination angles higher than 45° and few existing models capture all the effects of deviation angle, liquid flow rate, pressure, and pipe diameter. A new model is developed based on the physics of the onset of liquid film reversal, coupled with a new model for the liquid film thickness distribution around the pipe perimeter. It captures well the effects of deviation angle, liquid flow rate, gas and liquid density, viscosity, and pipe diameter on the critical gas velocity, outperforming all other existing models.
The experiments in this study provide new insights into the onset of liquid accumulation in large- diameter deviated wells. The new mechanics model fills the critical gap to enhance accuracy when predicting the onset of liquid loading especially for deviated and large-diameter wells. It can be easily implemented, which will benefit the industry practically. It is also applicable to gas condensate pipelines where smaller inclination angles exist.
With the recent tremendous development in algorithms, computations power and availability of the enormous amount of data, the implementation of machine learning approach has spurred the interest in oil and gas industry and brings the data science and analytics into the forefront of our future energy. The idea of using automated algorithms to determine the rock facies is not new. However, the recent advancement in machine learning methods encourages to further research and revisit the supervised classification tasks, discuss the methodological limits and further improve machine learning approach and classification algorithms in rock facies classification from well-logging measurements. This paper demonstrates training different machine learning algorithms to classify and predict the geological facies using well logs data. Previous and recent research was done using supervised learning to predict the geological facies.
This paper compares the results from the supervised learning algorithms, unsupervised learning algorithms as well as a neural network machine learning algorithm. We further propose an integrated approach to dataset processing and feature selection. The well logs data used in this paper are for wells in the Anadarko Basin, Kansas. The dataset is divided into training, testing and evaluating wells used for testing the model. The objective is to evaluate the algorithms and limitations of each algorithm. We speculate that a simple supervised learning algorithm can yield score higher than neural network algorithm depending on the model parameter selected. Analysis for the parameter selection was done for all the models, and the optimum parameter was used for the corresponding classifier.
Our proposed neural network algorithm results score slightly higher than the supervised learning classifiers when evaluated with the cross-validation test data. It is concluded that it is important to calculate the accuracy within the adjacent layers as there are no definite boundaries between the layers. Our results indicate that calculating the accuracy of prediction with taking account the adjacent layers, yield higher accuracy than calculating accuracy within each point. The proposed feed-forward neural network classifier trains using backpropagation (gradient descent) provides accuracy within adjacent layers of 88%. Our integrated approach of data processing along with the neural network classifier provides more satisfactory results for the classification and prediction problem. Our finding indicates that utilizing simple supervised learning with an optimum model parameter yield comparable scores as a complex neural network classifier.
Ibrahim Mohamed, Mohamed (Colorado School of Mines) | Ibrahim, Ahmed Farid (Apache Corporation) | Ibrahim, Mazher (Apache Corporation) | Pieprzica, Chester (Apache Corporation) | Ozkan, Erdal (Colorado School of Mines)
The instantaneous shut-in pressure (ISIP) serves as an indication of the excess pressure in the hydraulic fracture due to the effect of fluid viscosity and pressure required to break the formation at the fracture tip. The ISIP value will be close to or at the fracture propagation pressure and will be greater than the fracture pressure. The ISIP is often estimated to be the pressure after the pumps are shut down, and the beginning of a pressure decline. Many approaches have been developed to estimate the ISIP from the falloff data. The development of these approaches is attributed to the persistent trials due to the difficulty of quantifying the ISIP value accurately. Giving bottomhole pressures, ISIP can be estimated by subtracting the friction pressure drop from bottomhole pressure. This approach tends to overestimate the value of ISIP as it doesn't account for friction near the wellbore or through the perforations. Another common approach to estimate ISIP is by drawing a straight line on the early falloff portion of the Diagnostic Fracture Injection Tests (DFIT).
Previous studies show that the choice of ISIP affects the net pressure calculations, but not the slope of the derivative curves and the flow regime identification. This paper presents field cases where the values of ISIP affects the interpretation of the reservoir characteristics. Thus, the determination of accurate ISIP is very crucial.
This paper reviews the previously proposed approaches for determining the ISIP and provide a state of the art simple method to determine ISIP from non-ideal falloff data. The ISIP determined from the proposed method is verified by examination of the semi-log derivative plot, and the interpreted reservoir characteristics were found to be consistent with both field and lab observations. The method was validated using field DFITs falloff data from high-pressure dependent leakoff formations as well as formations that yield normal leakoff pressure dependent.
The novelty of the proposed method is in the simplicity of determination of ISIP and the consistency with the field observations. A number of field examples from the Barnett shale are illustrated using mechanisms previously proposed in the literature as well as the method presented in this paper. The later provided consistent ISIP values after multiple iterations. Subsequently, the reservoir characteristics and calculated parameters were uniform within the same pad of wells.
In the petroleum industry, references to a new science called Operations Research appeared in the literature in the late 1950s; however, over the last 60 years, use of Operations Research optimization techniques in the petroleum industry has been sporadic, resulting in vast untapped optimization opportunities. Some subsets of the petroleum industry have successfully used Operations Research optimization techniques for specific applications; however, when these techniques were not used by the industry, the stated reasons involved difficulty dealing with non-linearities and stochastic elements, insufficient computational power to solve realistic models, and the necessity for specialized knowledge of Operations Research optimization software and solvers. In this paper we provide a history of the use of Operations Research optimization methods in the petroleum industry by presenting a comprehensive review of papers that use these techniques. We focus on linear, nonlinear, integer, and mixed-integer optimization methods and the evolution of these models in the petroleum industry over time.
Joshi, Deep (Colorado School of Mines) | Eustes, Alfred (Colorado School of Mines) | Rostami, Jamal (Colorado School of Mines) | Gottschalk, Colby (Colorado School of Mines) | Dreyer, Christopher (Colorado School of Mines) | Liu, Wenpeng (Colorado School of Mines) | Zody, Zachary (Colorado School of Mines) | Bottini, Claire (Colorado School of Mines)
Water is considered the ‘oil of space’ with applications ranging from fuel production to colony consumption. Recent findings suggested the presence of water-ice in the Permanently shadowed craters on Lunar poles. This water present on the Moon and other planetary bodies can significantly bring down the cost of space exploration, fueling the colonization of the solar system. With low-resolution orbital data available, the next step is to drill and analyze samples from the Moon.
An extensive review of drilling systems designed by NASA was conducted focusing on the effect of different planetary environments on the drill design. Inspired by this and the drilling systems developed in the petroleum industry, an auger based rotary drilling rig was designed and fabricated with an extensive high-frequency data acquisition system, measuring all essential drilling parameters. Several analog rocks were cast with regolith simulant grout to replicate different subsurface geotechnical properties in the Lunar polar craters. The drill was tested on samples with different geotechnical properties to account for the varying properties expected in the Lunar poles.
Application of the drilling engineering concepts has resulted in the development of a robust drilling system capable of replicating drilling process for different planetary environments like the Moon and Mars. Using the data acquisition system on the rig, an advanced machine learning algorithm capable of processing and analyzing the real-time high-frequency drilling data to estimate a sample's geotechnical properties and water content was created. The evolving algorithm was developed based on initial drilling tests on homogenous and heterogeneous analogs. It was tested on samples with varying heterogeneity to estimate the geotechnical properties and the water content accurately. With some modifications, this algorithm can be applied in the Lunar and Martian missions to estimate the geotechnical properties in real-time, without the need to analyze the subsurface samples on the surface. This can result in a cost-effective exploration of water-ice resources on the Moon and Mars, kickstarting the space resources industry and the human colonization on those planetary bodies. The expertise of the drilling engineers in designing and executing wells in extreme terrestrial environments can help create significantly effective drilling systems for extraterrestrial environments.
This work details the design considerations to drill on the Moon and other planetary bodies focusing specifically on the application of drilling data to evaluate geotechnical properties and water content at Lunar polar conditions. The techniques developed here might pay a vital role in understanding the extent and composition of water-ice on the Moon, leading to efficient colonization of the solar system.
We present an assessment of the impact of low-salinity brine osmosis on oil recovery in liquid-rich shale reservoirs. The paper includes: (1) membrane behavior of shales when contacted by low-salinity brine, (2) numerical model of osmosis mass transport for low-salinity brine, and (3) enhanced oil recovery (EOR) potential of low-salinity osmosis in liquid-rich shale reservoirs. Capillary osmosis causes low-salinity brine to be imbibed into the shale matrix; thus, forcing expulsion of oil from the rock matrix. This oil recovery process is described by a multi-component mass transport mathematical model consisting of advective and molecular transport of water molecules and dissolved ions. In the transport model, the activity-corrected diffusion of the brine solution is used to calculate the volume of brine imbibed into a shale core sample and the resulting expelled oil. We used the mathematical model to match oil recovery from two carefully designed brine-imbibition experiments conducted at Colorado School of Mines. We have concluded that, in oil-wet shale reservoirs, low-salinity brine invasion of the rock matrix is by osmosis rather than capillary force. Thus, osmosis is the only imbibing force that drives the low salinity brine into the reservoir rock matrix. Furthermore, we believe brine osmosis can potentially enhance oil recovery by expelling oil out of the rock matrix and into the micro-and macro-fractures existing in the stimulated reservoir volume.
System instability prediction is essential when designing a production system and/or providing operational adjustment to maintain a stable production. The conventional system Nodal Analysis articulates that the system is unstable to the left of the minimum of the Outflow Performance Relationship (OPR) curve where the well loads up. However, recent data shows that there are stable production points on the left of the minimum of the OPR curve, especially for low permeability shale plays. In this work, a new practical model is presented for both conventional and unconventional wells using Nodal Analysis with a novel approach.
The new approach is based on the derivative analysis of the inflow performance relationship (IPR) and OPR at a nodal point of the bottom hole. Perturbation analysis is used to facilitate the explanation of the new model. It shows that the system is stable when the absolute value of slopes or derivatives of the IPR is greater than that of OPR. To evaluate this concept, transient numerical simulations were conducted using a commercial transient simulator at various IPR conditions, including different permeabilities, for both vertical and horizontal wells. Meanwhile, the concept is also compared with available experimental and field data.
The transient simulation and the available data presented in this study demonstrate that there are stable production operating points on the left of the minimum of the OPR curve. The system stability also depends on the reservoir permeability, i.e., the flow rate corresponding to the onset of instability decreases with decreasing permeability. The new approach predicts this trend well. Overall, the new model matches well with observation from the experiments, field data, and the transient numerical simulations.