Gong, Yiwen (The Ohio State University) | Mehana, Mohamed (Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, USA) | Xiong, Fengyang (The Ohio State University) | Xu, Feng (Research Institute of Petroleum Exploration and Development CO., LTD, CNPC) | El-Monier, Ilham (China National Oil and Gas Exploration and Development Corporation)
Rock elastic moduli are one of the major perspectives for the hydraulic fracturing design. Among all of them, Young's modulus and Poisson's ratio essentially control fracture aperture for the proppant placement. The objective of this work is to predict the elastic moduli by applying data mining techniques as a comparison to the experimental measurements. We have collected attributes representing the pore structure, mineralogy and geomechanical characteristics. We implemented classification techniques such as k-means, hierarchical and PAM (partition around medoids). PAM results in more evenly-distributed clusters compared to the rest. Artificial Neural Network (ANN) is used for regression. We formulated two scenarios; firstly, all the data is grouped into one group and the other involves performing the regression on the clustered data. Interestingly, both scenarios yield acceptable results. The classification results could guide the fracturing operations where clusters with high brittleness, low anisotropy and high microfracture intensity could be identified as fracture candidates. Still the main limitation to unleash the machine learning capabilities in this domain is the data scarcity
Almost simultaneously, advances were made in understanding both the processes within the source rock organic matter that accompany the generation and expulsion of hydrocarbons and in the acquisition, processing, and quantitative interpretation of 3D seismic data. In particular, as organic matter in shales in unconventional plays generates and expels hydrocarbons, porosity is formed in the organic matter and the organic matter becomes more dense and more brittle. As these changes are occurring at a micro-scale, extraction of hundreds of different attributes from a well-imaged 3D seismic volume has made it possible to observe changes at a macro-scale in seismic lines and horizons within that volume. Seismic attributes derived from pre-stack inversions yielding rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density, when calibrated with well log and/or core measurements, can be combined to calculate TOC, pore pressure, rigidity, and compressibility because these properties cause fundamental changes in how seismic waves travel through the rock.
Equally important, the escalation in computing power via methods such as machine learning, neural networks, and multivariate statistics has made it possible to interpret large amounts of data. All of these innovations have contributed to better identification of sweet spots within unconventional plays. Such sweet spots include areas with elevated TOC values, enhanced porosity, and zones that can be targeted for fracking.
One of the primary advantages of seismic data is that it provides information in those areas in between control points/wells. This information in turn helps operators to better select targets for wells and for landing zones. Carefully tied 3D seismic inversion and integration with petrophysical and rock data further allow for detailed characterization of unconventional reservoirs. The enhanced ability to identify the best potential drilling targets has significant economic implications in terms of risk reduction and improved chances to find economic prospects.
While 3D seismic data is being used routinely by numerous companies to predict the mechanical properties, density, and associated TOC of many formations, there is yet to be a direct link made between TOC loss, kerogen conversion, and the associated changes in rock properties. This work documents the importance of TOC loss during maturation and its effects on rock properties like porosity, density, brittleness, and how those advances coupled with the advances in quantitative interpretation of 3D seismic data are enabling the unconventional operators to predict location, thickness, landing zone, and sweet spots with appropriately acquired, processed, and interpreted 3D seismic. Meticulously calibrated 3D seismic inversion and integration with petrophysical and rock data permit detailed reservoir characterization of unconventional reservoirs.
Updated methods for the back calculation of original TOC have been developed using well logs, rock measurements, and 3D basin modeling to assist in locating and developing unconventional reservoirs. In addition, petrophysical measurements that reflect TOC and porosity and are related to fundamental properties controlling the seismic response can be extracted from the seismic reflection data. In turn, seismic attributes derived from pre-stack inversions yielding rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density, when calibrated with well log and/or core measurements, can be combined to estimate TOC, pore pressure, rigidity, and compressibility because these properties cause basic modifications in how seismic waves travel through the rock.
This study shows advancements in studies of: 1) TOC loss with increased thermal maturation, 2) how this loss affects the development of organic porosity, 3) how kerogen becomes denser, harder, and more brittle with increasing maturity, and 4) how recent developments in quantitative interpretation workflows for 3D seismic data facilitate estimation of TOC and determination of rock mechanical properties from shear (Vs) and compressional (Vp) velocities and density. Further integration of geochemical, geomechanical, and geophysical technologies and measurements will provide improved estimates of present-day TOC that can in turn be extended to relative maturity and percent conversion.
Examples provided in this work illustrate prediction of present-day TOC, porosity, density, and mechanical properties extracted from high fidelity pre-stack inversion. Pre-stack inversion along with machine learning can be used to predict rock properties such as porosity, TOC, organic matter quality, rigidity, and pressure and to correlate those properties back to well productivity for improved execution. Relating present TOC estimated from seismic to TOC loss and kerogen property changes with increasing maturity is possible by combining the results of these technologies.
Though analysis and inversion of painstakingly acquired modern 3D seismic data is capable of estimating porosity, TOC, matrix strength, and pore pressure, the latest work on rock property changes as hydrocarbons mature and are expelled isn't typically addressed in most studies. Increasing communication between disciplines might improve estimation of these properties and extend the capability to assess the extent of TOC loss during maturation and the porosity increases that accompany it. This ability is especially important in the intra-well regions where the potential of 3D seismic to extend data between control points enables better reserve estimates and high grading of acreage. After carefully calibrating a quantitative 3D seismic interpretation with a 3D basin modeling analysis of the source rock potential and maturity, an operator is better prepared to high grade acreage and attain the most economic development of unconventional resources.
The escalation in computing power means there are hundreds of different attributes that can be extracted or calculated from a well-imaged 3D seismic volume. Using quantitative calibration of fundamental geochemical measurements such as TOC, pyrolysis, and petrographic measurements of vitrinite reflectance that yield the quantity, quality, and maturity of organic matter in combination with well log and seismic data creates a model for identifying sweet spots and the areas in the target formation that exhibit high TOC, high porosity, and elevated brittleness. Further integration and calibration of changes occurring at the micro-level in organic matter in unconventional plays with their impact on the signatures of data at the macro-level can provide information on the types of hydrocarbons most likely to be found in these sweet spots as well as identifying which zone(s) in the target formation are most likely to be amenable to fracking. Used together, the advances outlined here result in a technological evolution that could have a substantial impact on: 1) the approach to and 2) the economics of the exploration and production of unconventional plays.
Hammon, Helen (Premier Oilfield Group) | Prather, Timothy (Premier Oilfield Group) | Rowe, Harry (Premier Oilfield Group) | Mainali, Pukar (Premier Oilfield Group) | Matheny, Mei (Premier Oilfield Group) | Krumm, Robert (Premier Oilfield Group)
The Latest Pennsylvanian and Early Permian (Wolfcamp, Dean, and Spraberry) interval of the Midland Basin, West Texas, represents a thick (often >1000 ft), mixed succession of shale, carbonate, and siltstone/sandstone lithologies that accumulated in a deep-water marine environment under variable hydrographic restriction. Because the succession is highly heterolithic, it is critical to understand and predict the stratigraphic and lateral variability in lithologic change and assess its impacts on reservoir properties. A highly-resolved (2-inch vertical) x-ray fluorescence-based chemostratigraphic study was undertaken on the Sun Oil D.E. Richards #1 drill core, recovered from Martin Co., TX. The core, while not continuous, contains “windows” of continuous sections of the upper Wolfcamp shale/siltstone through the lowermost Clearfork equivalent strata (Upper Leonard). XRF analysis for major and trace elements was conducted on the slabbed core face for 2567 sample intervals which were calibrated using a set of reference materials from a broad range of mudrock lithologies. In conjunction with XRF sampling, a subset of depth-matched sample powders (n = 229) was collected from the back of the core for mineralogical (XRD) and organic carbon analysis (LECO). A data refinement approach that incorporates elemental results from XRF and mineralogical results from XRD powders is developed to highlight element-mineral linkages and to establish a stoichiometry-derived mineralogy model from the 2-inch XRF data. The XRF-modeled mineralogy can be utilized to resolve sub-log-scale lithological variability and its impacts on rock strength, which are important characteristics to consider for completion optimization and overall drilling strategies in unconventional reservoirs.
Integration of XRD data with the 2-inch XRF data reveals that large-scale changes in elemental concentrations (%Al, Si/Al, %Ca, %Mg) can be interpreted as changes in mineralogical abundances of clays, quartz/clay, calcite, and dolomite, respectively. Furthermore, TOC values can be used to understand the organic variability present in each chemofacies found in this study. A discussion of the chemostratigraphy in the context of mineralogical changes, rock strength changes, and the selection of more detailed analyses (e.g., NMR, rock mechanics) will be undertaken.
Gong, Yiwen (The Ohio State University) | Mehana, Mohamed (University of Oklahoma) | El-Monier, Ilham (The Ohio State University) | Xu, Feng (Research Institute of Petroleum Exploration and Development Co. Ltd. CNPC / China National Oil and Gas Exploration and Development Corporation) | Xiong, Fengyang (The Ohio State University)
The accurate estimation of the elastic properties of the rock is of great importance for designing a successful hydraulic fracturing. Among these properties, Young's modulus and Poisson's ratio essentially control fracture aperture and conductivity. However, the fissile nature of the shale rock largely challenges the mechanical properties measurement using a cylindrical core sample. While the nanoindentation technology can be applied to measure small chips of rock fragment, but reproducible experiments are required to provide an unbiased estimation. Herein, we are proposing a machine learning approach to predict the elastic moduli. We utilized an ensemble of data mining techniques and a database that include both the mineralogy and pore characteristics. Our results indicate that K-Means clustering yields best performance on data classification than all other tested methods while the elastic moduli estimation from Artificial Neural Network (ANN)is most accurate than Support Vector Machine (SVM), Multivariate Linear Regression (MLR) and Multivariate Adaptive Regression Spine (MARS). The dimension reduction became essential when then input datasets are remarkably correlated. The supervised learning techniques with our proposed approach leverage the usability of the lab experiment data and overcome disadvantages of the traditional elastic moduli measurement. It also further lands the far-reaching guide for the fracturing design.
Machine learning have recently revolutionized the oil and gas industry (Alcocer and Rodrigues 2001, Al-Fattah and Startzman 2001, Kohli and Arora 2014, Okpo et al. 2016, Sinha et al. 2016, Tariq et al. 2017, Luo et al. 2018, Nande 2018, Rashidi et al. 2018, Sidaoui et al. 2018, Xu et al. 2019). As a data-rich industry, machine learning finds applications in every corner ranging from production forecast to drilling efficiency (Hegde and Gray 2017, Fulford et al. 2016). Given the significance of geomechanical properties of the rock, the volume of studies has attempted to leverage machine learning techniques. For instance, Li et al. (2018) developed a workflow implementing various machine learning algorithm to accurately provide an alternative to synthesize the sonic logs and geomechanical properties afterwards. In the same time, Hadi and Nygaard (2018) used Artificial Neural Network (ANN) to develop an empirical model to estimate the shear velocity from conventional logs. Another dimension was presented by Jain et al. (2015) where they proposed an approach to integrate both core and log spectroscopy which provided better estimations of the mineralogy.
Liquid CO2 fracturing (Dry CO2 fracturing) is an important solution to reduce the damage to water sensitive or low-pressure formations by water based fracturing fluids. The application of dry CO2 fracturing is limited by the requirement of high cost pressurized blending systems. Fracture scope is also limited by the equipment capacity and the carrying capacity of proppant by liquid CO2.
A quasi-dry CO2 fracturing method has been developed enabling the blending of CO2 viscosifier and proppant at ambient conditions. The method has eliminated the fracture scope limitation caused by the capacity of pressurized blending equipment and simplified the operational complexity making wider application of liquid CO2 fracturing possible. The main characteristics of the quasi-dry CO2 technology will be described in this paper.
Field test was conducted recently on two wells in one of the unconventional blocks in Ordos basin in October 2018. The fracturing operations were successful indicating that the new method is operationally feasible. In comparison, two offset wells were fractured one of which was with energized crosslinked gel and another with slick water/crosslinked gel system. The two offset wells did not have gas production after flowing back. The quasi-dry CO2 fractured wells had significant gas production and 60-90% of the water was recovered.
The new procedure significantly simplifies the operation and reduces the cost of liquid CO2 fracturing. It may find more applications in unconventional reservoirs and EOR with liquid CO2.
Hydraulic fracturing is an essential measure in developing tight and shale reservoirs. There are many reservoirs that are water sensitive. Using water based fracturing fluid may cause formation damage such as clay swelling and water blocking in Ordos basin and other areas in China. In some low-pressure reservoirs in this area, energized fluid such as CO2 or N2 are used to help fracturing fluid flow back in the past. In recent years, efforts have been put on the application of alternative fracturing fluids such as liquid CO2.
Rowe, Harry (Data Analytics Consultant / Premier Oilfield Group) | Mainali, Pukar (Premier Oilfield Group) | Nieto, Michael (Premier Oilfield Group) | Grillo, John (Premier Oilfield Group) | Rowe, Harry B. (Data Analytics Consultant)
The interpretation of large geochemical data sets (103 to 105 sample points) and their derivatives are developed, checked for veracity and relevance, optimized, and integrated with associated data sets to address questions regarding lithological heterogeneity, depositional continuity, depositional conditions, diagenesis, and brittleness. A 110-well Delaware Basin XRF-based geochemical data set, largely consisting of 10-feet-resolution cuttings samples that span much of the Wolfcamp through Delaware Mountain Group, are used as an example. Data workflows employing a suite of unsupervised learning techniques (e.g., PCA, HCA) are evaluated to determine the strengths/limitations of each technique, and their collective/comparative utility. Elemental results are used as inputs to stoichiometry-constrained element-to-mineral (E-M) models that yield useful inferences. The strengths of an E-M model rest on the accuracy of the ED-XRF calibration, sample quality, analytical prowess of the ED-XRF analyst, the overall rigorousness of the XRD technique and analyst employed, and the specific approach of the E-M model. Further to this point, derivatives of the XRF-based modeled mineralogy, such as a mineral brittleness index (mBI) and derived chemofacies, are only as good as the analytical underpinnings of the inputs. Modern core- and cuttings-based stratigraphic studies frequently incorporate an inorganic geochemical component, often acquired with portable energy-dispersive x-ray fluorescence (ED-XRF). Despite the analytical limitations of the ED-XRF approach, the use of this technique yields large, quantitative data sets collected at the length-scale of inches (cores) to feet (cuttings). In their raw elemental form, these data sets provide additional correlation and lithological control at scales just above (core), to just below (cuttings) the scale of most downhole log suites. The significance of this approach is that it can be used to 1) refine rock signatures in well logs, and 2) resolve questions regarding stratigraphic succession and correlation. While the initial focus of the study is to reconstruct the spatial distribution of lithologies at the scale of cuttings sample collection (10 feet), the overarching goal of the project is to optimize the interpretation of log signatures through the addition of data generated from the rock. This approach has cross-disciplinary implications, including refinement of petrophysical, geomechanical, and regional geological models. The modeled mineralogy from the chemostratigraphy results has direct implications for modeling fluid-rock compatibility and the overall completions process, including a more strategic selection of stage lengths.
Penghui, Su (PetroChina Research Institute of Petroleum Explorationand and Development) | Zhaohui, Xia (PetroChina Research Institute of Petroleum Explorationand and Development) | Ping, Wang (PetroChina Research Institute of Petroleum Explorationand and Development) | Liangchao, Qu (PetroChina Research Institute of Petroleum Explorationand and Development) | xiangwen, Kong (PetroChina Research Institute of Petroleum Explorationand and Development) | Wenguang, Zhao (PetroChina Research Institute of Petroleum Explorationand and Development)
Interest has spread to potential unconventional shale reservoirs in the last decades, and they have become an increasingly important source of hydrocarbon. Importantly, pore structure of shale has considerable effects on the storage, seepage and output of the fluids in shale reservoirs so that reliable fractal characteristics are essential. To better understand the evolution characteristics of pore structure for a shale gas condensate reservoir and their influence on liquid hydrocarbon occurrences and reservoir physical properties, we conducted high-pressure mercury intrusion tests (HPMIs), field emission scanning electron microscopies (FESEM), total organic carbon (TOC), Rock-Eval pyrolysis and saturation measurements on samples from the Duvernay formation. Furthermore, the fractal theory is applied to calculate the fractal dimension of the capillary pressure curves, and three fractal dimensions D1, D2 and D3 are obtained. The relationships among the characteristics of the Duvernay shale (TOC, organic matter maturity, fluid saturation), the pore structure parameters (permeability, porosity, median pore size), and the fractal dimensions were investigated.
The results show that the fractal dimension D1 ranges from 2.44 to 2.85, D2 ranges from 2.09 to 2.15 and D3 ranges from 2.35 to 2.48. D2 and D3 have a good positive correlation. The pore system studied mainly consists of organic pores and microfractures, with the percentage of micropores being 50.38%. TOC has a positive relationship with porosity and D3 due to the development of organic pores. D3 has a positive correlation with gas saturation. With increased D3, median pore size shows a decreasing trend and an increase in permeability and porosity, demonstrating that D3 has a large effect on pore size distribution and the heterogeneity of pore size. In general, D3 has a better correlation with petrophysical and petrochemical parameters. Fractal theory can be applied to better understand the pore evolution, pore size distribution and fluid storage capacity of shale reservoirs.
Huang, Hai (Xi'an Shiyou University, Shaanxi Key Laboratory of Advanced Stimulation Technology for Oil & Gas Reservoirs) | Babadagli, Tayfun (University of Alberta) | Chen, Xin (University of Alberta) | Li, Huazhou (University of Alberta)
Tight sands are abundant in nanopores leading to a high capillary pressure and normally a low fluid injectivity. As such, spontaneous imbibition might be an effective mechanism for improving oil recovery from tight sands after fracturing. The chemical agents added to the injected water can alter the interfacial properties, which could help further enhance the oil recovery by spontaneous imbibition. This study explores the possibility of using novel chemicals to enhance oil recovery from tight sands via spontaneous imbibition. We experimentally examine the effects of more than ten different chemical agents on spontaneous imbibition, including a cationic surfactant (C12TAB), two anionic surfactants (O242 and O342), an ionic liquid (BMMIM BF4), a high pH solution (NaBO2), and a series of house-made deep eutectic solvents (DES3-7, 9, 11 and 14). Experimental results indicate that the ionic liquid and cationic surfactant used in this study are detrimental to spontaneous imbibition and decrease the oil recovery from tight sands. The high pH NaBO2 solution does not demonstrate significant effect on improving oil recovery, even though it significantly reduces oil-water interfacial tension (IFT). The anionic surfactants (O242 and O342) are effective in enhancing oil recovery from tight sands through oil-water IFT reduction and emulsification effects. The DESs drive the rock surface to be more water-wet and a specific formulation (DES9) leads to much improvement on oil recovery under counter-current imbibition condition. This preliminary study would provide some knowledge about how to optimize the selection of chemicals for improving oil recovery from tight reservoirs.
Wu, Zengzhi (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Zou, Hongjiang (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Wang, Yugong (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Wu, Long (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Li, Yong (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Xu, Yang (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Wang, Renfeng (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Meng, QingCong (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Jiang, Wenxue (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Wang, Suoliang (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Li, Shan (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute) | Li, Dan (CNPC Chuanqing Drilling Engineering Co. Ltd., Drilling & Production Engineering Technology Research Institute)
With the development of Chang Qing Oilfield, the following technical problems are faced: the increasing proportion of the low-production and low-efficiency wells, the worse production of pertinence and effectiveness in conventional retreatments and a large amount of remaining oil between wells and layers. In this case, the technique of radial fracture network fracturing and deep plugging has been proposed. Meanwhile, the matching products which include variable viscosity diverting acid, micro expansion high strength plugging agent and temporary plugging agent (oil soluble, water soluble and anti-scale) have been developed as well. Nowadays, the retreatment technology has been successfully tested and popularized in Chang Qing oilfield. Compared with the conventional retreatments, these two technologies have remarkable effect on increasing production and prospective application.
Xi, Shengli (PetroChina Changqing Oilfield Company) | Hou, Yuting (PetroChina Changqing Oilfield Company) | Li, Xianwen (PetroChina Changqing Oilfield Company) | Hu, Xifeng (PetroChina Changqing Oilfield Company) | Liu, Peng (Schlumberger) | Zhao, Xianran (Schlumberger)
The Triassic Yanchang formation is rich in tight oil resource at Ordos Basin. The oil sandstone and oil shale of Chang 7 member are widely spread in the basin and have huge potential in oil production. Due to low porosity and low permeability, producing oil from tight oil reservoir depends on hydraulic fracturing. A successful hydraulic fracture requires accurate estimations of horizontal stresses and rock elastic properties in design and operation.
Chang 7-2 is shale and sandstone interbed reservoir and Chang 7-3 is shale oil reservoir with lamination sedimentary structure. The rocks with lamination structure are very anisotropic, and it needs to be considered in computation of horizontal stresses and rock elastic properties.
In this paper, we present a case study to illustrate the advantages of anisotropic geomechanics model. Anisotropic horizontal stresses and rock elastic properties were calculated and used in hydraulic fracturing design. The perforation intervals were selected at depths with low stress magnitude based on stress profile. The perforations efficiency was analyzed, and perforation interval with low efficiency was removed. Major stimulation operation parameters, total volume, proppant volume and slurry rate, were optimized with anisotropic geomechanics model. Fracturing operation results showed that the total volume was decreased by 16.5%, proppant pumped increased by 11.4% and daily oil production increased by 73.7%. This case study demonstrated that anisotropic geomechanics model help to improve operation efficiency and increase oil production.