eagle ford
Recently Published Documents





2022 ◽  
Dante Guerra ◽  
Deron Arceneaux ◽  
Ding Zhu ◽  
A. D. Hill

Abstract Presently, two-phase flow behavior through propped and unpropped fractures is poorly understood, and due to this fact, reservoir modeling using numerical simulation for the domain that contains fractures typically assumes straight-line relative permeability curves and zero capillary pressure in the fractures. However, there have been several studies demonstrating that both viscous and capillary dominated flow can be expected in fractured reservoirs, where non-linear fracture relative permeabilities must be used to accurately model these reservoirs. The objective of this study is to develop an understanding of the relative permeability of oil-water systems in fractures through experimental study. The experimental measurements conducted in this study were done using downhole cores from the Wolfcamp and the Eagle Ford Shale formations. The cores were cut to 1.5-in diameter and 6-in length testing samples. The specimens are saw-cut to generate a fracture along each sample first, and then conditioned in the reservoir fluid at the reservoir temperature for a minimum of 30 days prior to any testing. Wolfcamp and Eagle Ford formation oil and reconstituted brine with and without surfactants are used as the test fluids. The measurements were recorded at effective fracture closure stress and reservoir temperature. Also, real-time measurements of density, pressure, and flow rate are recorded throughout the duration of each test. Fluid saturation within the fracture was calculated using the mass continuity equation. The oil-water relative permeability was measured using the steady-state method. All measurements were conducted at reservoir temperature and at representative effective fracture closure stress. The data from the experimental measurements was analyzed using Darcy's law, and a clear relationship between relative permeability and saturation was observed. The calculated relative permeability curves closely follow the generalized Brooks-Corey correlation for oil-water systems. Furthermore, there was a significant difference in the relative permeability curves between the oil-water only systems and the oil-water surfactant systems. The result of this study is useful for estimating the expected oil production more realistically. It also provides information about the effect of surfactants on oil-water relative permeability for optimal design of fracture fluids.

2022 ◽  
Abdulrahim K. Al Mulhim ◽  
Jennifer L. Miskimins ◽  
Ali Tura

Abstract This paper focuses on optimizing future well landing zones and their corresponding hydraulic fracture treatments in the Eagle Ford shale play. The optimum landing zone and stimulation treatment were determined by analyzing multiple landing zone options, including the lower Austin Chalk, Eagle Ford, and Pepper Shale, with several hydraulic fracturing treatment possibilities. Fracturing fluids and their volume, proppant size, and cluster spacing were investigated to determine the optimum hydraulic fracturing treatment for the subject geologic area. Ranges of 75,000 to 300,000 gallons of pure gel, pure slickwater, and hybrid fracturing fluids along with 20/40, 30/50, 40/70, and 100 mesh proppant were tested. Cluster spacing of twenty feet to eighty feet were also sensitized in this study. A fully three-dimensional hydraulic fracture modeling software was used to develop a geological and geomechanical model of the studied area. The generated model was calibrated with available field data to ensure that the model reflects the area's geological and geomechanical characteristics. The developed model was used to create fracture results for each sensitized parameter. Production analysis was performed for all fracture models to determine the optimum landing zone and fracturing treatment implications. The study shows that the Eagle Ford had better production than the lower Austin Chalk in the subject area. The Pepper Shale had the highest potential hydrocarbon production, around 326 Mbbl cumulative, when fractured with a pure gel treatment. The analyses showed that a hybrid treatment with 70% gel and 30% slickwater yielded the optimum production due to the treatment economics even though the highest production was obtained using the pure gel. Treating the formation with larger proppant provided better production than smaller proppant due to conductivity concerns associated with damaging mechanisms in the studied area. Since increasing the volume above 175,000 gallons caused a negligible increase in the production, 175,000 gallons of fracturing fluid per stage appeared to be the optimum fracturing fluid volume. Thirty-foot cluster spacing was the optimum spacing in the study area. Overall, the study suggests that oil production can be improved in the Eagle Ford study area through a detailed workflow development and optimization process. The hydraulic fracture treatment and landing zone optimization workflow ensures optimum hydrocarbon extraction from the study area. The developed workflow can be applied to new unconventional plays instead of using trial and error methods.

2022 ◽  
pp. 1-62
Ajit K. Sahoo ◽  
Vikram Vishal ◽  
Mukul Srivastava

Placement of the horizontal well within the best landing zone is critical to maximize well productivity, thus identification of the best landing zone is important. This paper illustrates an integrated semi-analytical workflow to carry out the stratigraphic characterization of the Eagle Ford shale to identify the best landing zone. The objective of this work is twofold: 1) to establish a workflow for stratigraphic characterization and 2) to understand the local level variability in the well performance.To establish the workflow, we have used the production data, petrophysical information and regional reservoir property maps. As a first step of the workflow, we subdivided the Eagle Ford shale into nine smaller stratigraphic units using the wireline signatures and outcrop study. In the second step, we have used statistical methods such as linear regression, fuzzy groups and theory of granularity to capture the relationship between the geological parameters and the well performances. In this step, we identified volume of clay (Vclay), hydrocarbon filled porosity (HCFP) and total organic carbon (TOC) as key drivers of the well performance. In the third step, we characterized the nine smaller units and identified four stratigraphic units as good reservoirs with two being the best due to their low Vclay, high HCFP and high TOC content.Finally, we reviewed the well paths of four horizontal wells with respect to the best stratigraphic units. We observed that production behavior of these wells is possibly driven by their lateral placement. The better producing wells are placed within the middle of the best stratigraphic units whereas the poor wells are going out the best stratigraphic units. This investigation provides a case study that demonstrates the importance of integrating datasets to identify best landing zones and the suggested workflow can be applied to other areas and reservoirs to better identify targetable zones.

2021 ◽  
Clay Kurison ◽  
Ahmed M. Hakami ◽  
Sadi H. Kuleli

Abstract Unconventional shale reservoirs are characterized by low porosity and ultra-low permeability. Natural fractures are known to be present and considered a critical factor for the enhanced post-stimulation productivity. Accounting for natural fractures with existing techniques has not been widely adopted owing to their complexity or lack of validation. Ongoing research efforts are striving to understand how natural fractures can be accounted for and accurately modeled in fluid flow of the subject reservoirs. This study utilized Eagle Ford well data comprising reservoir properties, stimulation metrics, production, microseismicity and permeability measurements from a core plug. The methodology comprised use of production data to extract a linear flow regime parameter. This was coupled with fracture geometry, predicted from hydraulic fracture modeling and microseismicity, to estimate the system permeability. From interpreting microseismic events as slips on critically stressed natural fractures, explicit modeling incorporating a discrete fracture network (DFN) assumed activated natural fractures supplement conductive reservoir contact area. Thus, allowed the estimation of matrix permeability. For validation, the aforementioned was compared with core plug permeability measurements. Results from modeling of planar hydraulic fractures, with microseismicity as validation, predicted planar fracture geometry which when coupled with the linear flow parameter resulted in a system permeability. Incorporation of DFNs to account for activated natural fractures yielded matrix permeability in picodarcy range. A review of laboratory permeability measurements exhibited stress dependence with the value at the maximum experimental confining pressure of 4000 psi in the same range as the computed system permeability. However, the confining pressures used in the experiments were less than the in situ effective stress. Correction for representative stress yielded an ultra-low matrix permeability in the same range as the DFN-based picodarcy matrix permeability. Thus, supporting the adopted drainage architecture and often suggested role of natural fractures in shale reservoir fluid flow. This study presents a multi-discipline workflow to account for natural fractures, and contributes to understanding that will improve laboratory petrophysics and the overall reservoir characterization of the subject reservoirs. Given that the Eagle Ford is an analogue of emerging shales elsewhere, results from this study can be widely adopted.

2021 ◽  
Jodel Cornelio ◽  
Syamil Mohd Razak ◽  
Atefeh Jahandideh ◽  
Behnam Jafarpour ◽  
Young Cho ◽  

Abstract Transfer learning is a machine learning concept whereby the knowledge gained (e.g., a model developed) in one task can be transferred (applied) to solve a different but related task. In the context of unconventional reservoirs, the concept can be used to transfer a machine learning model that is learned from data in one field (or shale play) to another, thereby significantly reducing the data needs and efforts to build a new model from scratch. In this work, we study the feasibility of developing deep learning models that can capture and transfer common features in a rich dataset pertaining to a mature unconventional play to enable production prediction in a new unconventional play with limited available data. The focus in this work is on method development using simulated data that correspond to the Bakken and Eagle Ford Shale Plays as two different unconventional plays in the US. We use formation and completion parameter ranges that correspond to the Bakken play with their simulated production responses to explore different approaches for training neural network models that enable transfer learning to predict production responses of input parameters corresponding to the Eagle Ford play (previously unseen input parameters). We explore different schemes by accessing the internal components of the model to extrapolate and categorize salient features that are represented in the trained neural network. Ultimately, our goal is to use these new mechanisms to enable effective sharing and reuse of discovered features from one unconventional well to another. To extract salient trends from formation and completion input parameters and their corresponding simulated production responses, we use deep learning architectures that consist of convolutional encoder-decoder networks. The architecture is then trained with rich simulated data from one field to generate a robust mapping between the input and the output feature spaces. The "learned" parameters from this network can then be "transferred" to develop a different predictive model for another field that may lack sufficient historical data. The results show that using standard training approaches, a neural network model that is trained with sufficiently large data samples from Bakken could produce reliable prediction models for typical wells that may be found in that field. The same neural network, however, could not produce reliable predictions for a typical Eagle Ford well. Furthermore, we observe that a neural network trained with insufficient data samples from Eagle Ford produces a poor prediction model for typical wells that may be found in Eagle Ford. However, when extrapolated feature components of the Bakken neural network were integrated into the training process of the Eagle Ford neural network, the resulting predictions for typical Eagle Ford wells improved significantly. Moreover, we observe that the ability to transfer learning can improve when specialized training strategies are adopted to enable transfer learning. Using several numerical experiments, the paper presents and assesses various transfer learning strategies to predict the production performance of unconventional wells in a new area with limited information by integrating knowledge from more mature plays.

2021 ◽  
Jyun-Syung Tsau ◽  
Qinwen Fu ◽  
Reza Ghahfarokhi Barati ◽  
J. Zaghloul ◽  
A. Baldwin ◽  

Abstract The hydrocarbon gas huff and puff (HnP) technique has been used to improve oil production in unconventional oil reservoirs where excess capacity of produced gas is available and hydrocarbon prices are in a range to result in an economically viable case. Eagle Ford (EF) is one of the largest unconventional oil plays in the United State where HnP has been applied for enhanced oil recovery (EOR) at reservoirs within various oil windows. Our previously published Huff-n-puff results on dead oil with produced gas from Eagle Ford (EF) showed the recovery factor of hydrocarbon varying from 40 to 58%. The objective of this paper is to extend the experiments to live oil with EF core plugs to investigate the mechanisms of HnP which are affected by the composition of injected gas and resident oil, injection and soaking time as well as injection/depletion pressure gradient. Eagle Ford live oil and natural gas produced from the target area were used for HnP tests. Four representative core plugs were used with the tests conducted at reservoir conditions (125 °C and 3,500 psi). The live oil experiments with four reservoir core plugs showed an improvement in oil recovery with recovery factor (RF) varying from 19.5 to 33 % in six cycles of HnP, whereas the primary depletion on the same core plug showed RF below 11 %. A lower recovery factor of HnP from live oil saturated core in this study was observed as compared to dead oil saturated core reported in a previous publication. It is attributed to a lesser diffusion effect on mass transfer between injected gas and resident oil when the core is saturated with live oil. This behavior is displayed by the pressure decline curve during the first soaking period. A sharper diffusion pressure decline occurred in the dead oil saturated core plug where a higher concentration gradient between injected gas and resident oil drives a faster gas transport into the oil due to the molecular diffusion during the soaking period.

2021 ◽  
Cyrus Ashayeri ◽  
Birendra Jha

Abstract Decision making in new fields with little data available relies heavily on physics-based simulation models. However, due to a lack of full understanding of the physical processes governing flow in the unconventional resources, data-driven modeling has emerged as an alternative and complimentary tool to create recovery forecasts that honor the available data. Transfer Learning provides an opportunity to start early-stage analysis of the asset before adequate data becomes available. New challenges in the energy industry as well as shifting dynamics in both domestic and global supply and demand has encouraged some of the petroleum exporting countries in the Middle East to strategize the development of unconventional resources. In this research we have developed a data-driven Transfer Learning framework that allows the basin-wide assessment of new shale gas and tight oil prospects. The proposed Transfer Learning method is developed on real-world data from several thousand horizontal multistage wells in the Eagle Ford super-basin in South Texas. In this method we have integrated reservoir engineering domain expertise in the data pre-processing and feature generation steps. We have also considered the temporal and spatial balancing of the training data to assure that the predictive models honor the real practice of unconventional field development. Our full cycle Transfer Learning workflow consists of dimensionality reduction and unsupervised clustering, supervised learning, and hyperparameter fine-tuning. This workflow enables reservoir engineers to experiment with multiple hypothetical scenarios and observe the impact of additional data in the learning process. We use the developed workflow to examine the performance of a data-driven model of the Eagle Ford Basin on potential plays in the Middle East. Existence of all liquid types of oil, condensate and dry gas in the Eagle Ford has resulted in training a model flexible enough to be tested on various types of assets in a new location. We first present the successful deployment of our model within the Eagle Ford. Next, we use the information from major formations such as Tuwaiq Mountain and Hanifa and show the value of a pre-existing model from a fully-developed shale play on achieving acceptable accuracies with minimal information available in a new field. Our model is developed by data types with relatively low resolution that minimizes overfitting effects and allows generalization to different geologies with basin-wide accuracy. This approach allows conducting accelerated assessment of various sections of a large asset to enhance field development planning processes. This is a first example of such an effort on a basin scale that examines the effectiveness of Transfer Learning on some of the major unconventional plays in the Middle East region. This workflow allows investigating the relationship among geologic and petrophysical variables, drilling and completion parameters, and productivity of a large group of wells in a new asset.

Sign in / Sign up

Export Citation Format

Share Document