Currently many lean gas EOR pilot projects are implemented in Eagle Ford shale. The major component of lean gas is methane. From the field feedback, there is always large discrepancy between production forecast (or reservoir simulation) and the field results. The natural fracture system is complex and the communication between natural fracture, matrix and hydraulic fractures is even more complicated. Comparing connecting natural fracture between wells with short and low fracture conductivity, the well interference and the resulted optimal well spacing significantly change. In this study, according to field feedback, some connecting natural fractures with high fracture conductivity are mapped between wells to better represent the field geology and production status.
A composition reservoir simulation model is built for Eagle Ford shale. Typical production curve of Eagle Ford shale is matched for the first three years of primary production, lean gas Huff and Puff (HNP) process is simulated for the next three years for the parent wells and child wells. As the main composition of lean gas is methane, different methane adsorption effects are quantified between wells to investigate its influence on production. For lean gas Huff and Puff process, normally the Minimum Miscibility Pressure (MMP) is above 4000 psi. During lean gas cycling process, methane is adsorbed and desorbed, and the effective methane amount used to enhance miscibility between gas and oil phases is reduced, thus the reservoir pressure is not elevated to as high as no gas adsorption case. The methane adsorption effect significantly affects the oil and gas production. Relative permeability hysteresis and capillary pressure hysteresis (gas trapping effect) are the first time systematically studied to quantify the gas EOR performance in pad level production of unconventional reservoirs. Considering gas trapping effects, the cumulative gas injection amount and production amount is much better matched to the field pilot results. The oil incremental benefit of gas Huff and puff process considering gas trapping effect is quantified.
To our best knowledge, this is the first time that both methane adsorption and gas trapping effects are studied for pad level Huff and Puff process with the realistic connecting natural fractures between wells. Better matching of the production data with pilot results confirms the successful application of these two mechanisms. Considering the realistic complex natural fracture effects also greatly contributes to the correct production forecast and efficient design of gas EOR project for unconventional reservoirs such as Eagle Ford shale and other major basins.
Kolawole, Oladoyin (Texas Tech University) | Esmaeilpour, Sajjad (Texas Tech University) | Hunky, Rabia (University of Tripoli, Libya) | Saleh, Laila (University of Tripoli, Libya) | Ali-Alhaj, Haiat K. (University of Tripoli, Libya) | Marghani, Mouna (College of Engineering Technology, Libya)
Due to technical and economical limitations of hydrofracturing operations in unconventional reservoirs worldwide, an optimized hydraulic fracturing design is critical to achieve a successful well stimulation operation. The number and length of perforation clusters in each stage, proppant and fluid frac compatibilities, and optimum spacing, are some of the challenges inhibiting successful hydraulic fracturing operations across the world. In this study, we aim to investigate the effects of varying fracture-cluster lengths, proppant types and sizes, and frac fluid types, on hydraulic fracturing treatments using 2D and 3D simulation models. Our study relies on petrophysical log data, geologic data, well data, reservoir data, and production data from a well in the Eagle Ford shale formation. Firstly, we created stress log and developed a stress profile to determine fracture initiation location and orientation. We selected different combinations of frac-fluids and proppant types, 5 fracture-cluster lengths in a single stage, 2 different spacing lengths between fracture clusters, determined the maximum allowable treatment pressure, and selected an ideal fracture propagation model. Secondly, we selected an optimum treatment size, followed by performing the production forecast and the net present value analyses. In the third task, we determine the optimal specifications of the fracturing fluid and proppant, fluid volume and proppant weight, followed by creating a schedule for fluid injection and proppant mixing, and injection pressure profile prediction. Our results show that greater cluster lengths provide better and optimized hydraulic fracture treatment in some cases because of greater proppant coverage, fracture conductivity, stimulated reservoir volume (SRV), fracture half-length, and propped length. In the results obtained from varying proppant sizes and frac fluids in cases 2 and 3, we observed that Northern White 30-50 proppant produced a lower fracture conductivity (average of 2.0 md-ft) and fracture half-length (91 to 123 ft), in comparison with proppant composed of Badger 100 mesh and RC Sand PC 16-30, which achieved a greater fracture conductivity (> 3.0 md-ft) and fracture half-length (129 to 148 ft). In the 3 cases we investigated, we observed that stress shadowing and interference hindered the growing fractures which eventually led to a collapse of some fractures. We believe that our preliminary study will provide valuable critical decision-making during hydraulic fracturing operations in various unconventional formations across the world.
Hydrocarbon production from shale formation has become an essential part of the global energy supply in the past decade. The life of a project in an unconventional play significantly depends on the prediction of Estimated Ultimate Recovery (EUR). However, the conventional methodology to predict EUR becomes less accurate for shale formations, which significantly affects the economics returns of projects in unconventional plays. The objective of this article is to investigate the most important independent variables, including petrophysics and completion parameters, to estimate EUR by the machine learning algorithm. A novel machine learning model based on Random Forest Regression is introduced to predict EUR and to rank the importance of the independent variables.
In this article, production/petrophysics/engineering/ data with more than 25 variables from 4000 wells in Eagle Ford is summarized for analysis. The data is collected from production monitoring, well logging, well testing, seismic interpretation and lab experiments. This paper has three major components. Firstly, a multivariate linear regression model is created to predict the overall EUR. Secondly, the spatial autocorrelation analysis is carried out to identify whether spatial variables could affect the accuracy of the multivariate regression model. Thirdly, the Random Forest Regression models are trained to examine their reliability in predicting EUR with spatially autocorrelated data. The importance of key predictors is also identified. The final models are tuned with optimized hyperparameters. Through the article, the predictive capabilities of each Random Forest Regression model are discussed in detail to understand the physics behind unconventional hydrocarbon production mechanisms.
The results and workflow presented in this paper are insightful and novel. Firstly, we test the multivariate regression analysis with all the petrophysics and completion variables using the backward elimination method. This widely used model has a limitation of excluding the spatial information. In order to identify the impact of spatial variable, we calculate the Moran's Index and find out that the data in this study is clustered or spatially autocorrelated. The p-value for EUR, Oil EUR and Gas EUR are 0.000002, 0.000000 and 0.12, which all reject the null hypothesis that the data is randomly distributed. To include the spatial information in the prediction, we use advanced machine learning technology, Random Forest, to predict the EUR with a combination of petrophysics, completion variables and spatial information. The key variables to predict EUR, Oil EUR and Gas EUR by the Random Forest Regression are identified. However, the importance of the key variables to predict Oil EUR and Gas EUR are different. Therefore, we split the overall EUR Random Forest Regression model (57% explained) into two prediction models, one for Oil EUR prediction and one for Gas EUR prediction. The Gas EUR Random Forest Regression model has better performance (76% explained) compared to the Oil EUR Random Forest Regression model (60% explained).
This study provides a deeper understanding of unconventional hydrocarbon production prediction from a big data perspective, and proposes a novel and reliable machine-learning model to predict EUR to evaluate economic returns in Eagle Ford. Compared to the traditional multivariate regression model, our Random Forest Regression models are more reliable. In addition, the Random Forest technique is able to rank the importance of the relevant independent variables, and the rank of importance can be applied to guide and to improve data collection and model training for further study on this topic. The workflow presented in this article can be also used to train data for other unconventional resource plays.
Texas regulators rejected a rare challenge to gas flaring in the state after an oil company argued that a flaring ban would force it to shut in wells, damaging the reservoir and reducing future oil production. There is every reason to believe that enhanced oil recovery through huff-and-puff injections in US tight-oil plays could be a technical success across large numbers of wells. However, widespread economic success remains uncertain. This paper demonstrates how engineers can take advantage of their most-detailed completions and geomechanical data by identifying trends arising from past detailed treatment analyses. This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells.
This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells. The Eagle Ford formation has produced approximately 2 billion bbl of oil during the last 7 years, yet its potential may be even greater. Using improved oil-recovery (IOR) methods could result in billions of additional barrels of production. Shale EOR Works, But Will It Make a Difference? The promise of getting 30% more oil production from shale wells has set off a race by companies trying to see if they can replicate what EOG has done.
This paper studies the technical and economic viability of this EOR technique in Eagle Ford shale reservoirs using natural gas injection, generally after some period of primary depletion, typically through long, hydraulically fractured horizontal-reach wells. The Eagle Ford formation has produced approximately 2 billion bbl of oil during the last 7 years, yet its potential may be even greater. Using improved oil-recovery (IOR) methods could result in billions of additional barrels of production.
This deal will form the second-largest producer in Colorado’s DJ Basin. The combined company will produce more than 100,000 BOE/D from the Permian Basin and Eagle Ford Shale and is switching its focus to “mega-pad” developments. The deal consists of stakes in nine shallow-water producing fields covering 108,000 gross acres in 10–50 m of water. The combination will create one of the Haynesville Shale’s top gas producers, tripling Comstock’s Haynesville-Bossier acreage. Murphy Oil to Buy Deepwater US Gulf Assets for up to $1.625 Billion The El Dorado, Arkansas-based Murphy has quickly found a home for some of the cash it will receive from the sale of its Malaysia business.
The shale sector is making moves to consolidate amid investor pressure to increase cash flow. This deal will form the second-largest producer in Colorado’s DJ Basin. The combined company will produce more than 100,000 BOE/D from the Permian Basin and Eagle Ford Shale and is switching its focus to “mega-pad” developments. The deal consists of stakes in nine shallow-water producing fields covering 108,000 gross acres in 10–50 m of water. The combination will create one of the Haynesville Shale’s top gas producers, tripling Comstock’s Haynesville-Bossier acreage.
Sanguinito, Sean (National Energy Technology Laboratory) | Cvetic, Patricia (National Energy Technology Laboratory) | Goodman, Angela (National Energy Technology Laboratory) | Kutchko, Barbara (National Energy Technology Laboratory) | Natesakhawat, Sittichai (National Energy Technology Laboratory)
It is becoming increasingly important to expand the fundamental understanding of geochemical interactions between CO2, fluids, and shale. These interactions will significantly impact the processes of 1) storing CO2 in hydraulically fractured shale formations, 2) using CO2 as a fracturing agent, and 3) enhancing hydrocarbon recovery in shales via CO2 flooding. In this work, we use in-situ Fourier Transform infrared spectroscopy (FT-IR), feature relocation scanning electron microscopy (SEM), and surface area and pore size analysis using volumetric gas sorption and density function theory (DFT) methods to characterize and quantify the reactions that occur between CO2, fluids, and shale. Several shale samples from across the U.S. were analyzed including the Marcellus, Utica, and Eagle Ford Shales. CO2 will be injected into shale formations where it will interact with shale surfaces (i.e. clays, organic matter), in-situ fluids (i.e. natural brines), and previously injected fracturing fluid. Currently, it is assumed that dry supercritical CO2 does not interact with or have any impact on reservoir rocks or seals. Our suite of measurements show CO2 interaction with clay and kerogen components of the shale, reactivity and etching of carbonate, and changes in pore sizes at the meso- and micro-scale. Very few studies are taking into account the reactivity of CO2 and fluids in the reservoir. The reactions that occur between CO2, fluids, and the shale may alter petrophysical properties such as porosity and permeability which may alter flow pathways potentially impacting the storage permeance of CO2 and the effectiveness of CO2 to behave as a fracturing agent or to mobilize hydrocarbons.
With increasing awareness and concern of CO2 emissions and climate change, there has been a shift in research efforts to evaluate the potential of shales to be used as CO2 storage reservoirs and effective natural seals for CO2 or hydrocarbons (Orr, F.M., 2009a.; Orr, F.M., 2009b; Romanov et al., 2015; Levine et al., 2016, Bacon et al., 2015). Current research is underway to determine the fundamental understanding of geochemical interactions between CO2, fluids, and shale. Fluids, such as formation fluids and fracturing fluids, can react with the CO2 and shale interface to alter formation properties (Jun, Y et al., 2013; Dieterich et al., 2016). This geochemical alteration of shale has been reported to directly affect porosity, permeability, flow paths, and integrity of the wellbore, seal, and formation (DePaolo and Cole, 2013). Additionally, the storage temperature and pressure conditions and the composition and chemistry of brine solution and hydraulic fracturing fluid have an impact on the geochemical alteration of the shale (specifically dissolution).
Low frequency Nuclear Magnetic Resonance (NMR) spectrometers have been used extensively to study the cores from various shale plays in the US. However, in the realm of high frequency NMR measurements, these high frequency NMR studies are quite limited. Usually, the studies conducted using high frequency NMR spectrometer are performed on crushed or very small size core samples, which may not represent the real reservoir conditions. We use core samples in dimensions that are comparable in size to low field NMR and studies in addition to crushed core samples to obtain a comparative analysis in this study.
NMR measurements provide valuable insight in the characterization effort of shale plays. Since the conventional petrophysical methods of calculating the Bulk Volume Water (BVW) and Free Fluid Index (FFI) are not accurate enough, NMR data is indispensable to quantify these petrophysical parameters. NMR provides guidance in determining the organic matter hosted porosity and the bulk volume fluids held within the defined porosity, as these contribute towards the negative reservoir quality indices. Therefore, there has been extensive research using low field NMR laboratory and field measurements in shale formations. However, due to complex nanoscale structure of the tight shale formations, higher resolution is needed that can be accomplished with higher frequency measurements. Therefore, the high field high frequency NMR results of the shale samples enhances our understanding of the various pore systems in the shale formations and the bulk volume of the fluids held within these different pore systems helping us in identifying the sweet spots in the unconventional play studied.
2D NMR T1-T2 maps have been collected for preserved Eagle Ford core samples in dry and saturated with different fluids. The effect of the various saturating fluids in the experiments have been investigated on the organic and inorganic matter hosted porosity using the 2D NMR T1-T2 maps. These maps help to understand how the various saturating fluids interact with the different pore systems in shales, indicating the bulk and irreducible volume of these respective fluids held within different pore systems. It helps in delineating the way in which these pore systems contribute towards the positive and negative reservoir quality indices.