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Chen, Chi (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Shouxin (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lu, Cong (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Wang, Kun (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Lai, Jie (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University) | Liu, Yuxuan (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University)
Abstract Hydraulic fracturing technology provides a guarantee for effective production increase and economic exploitation of shale gas wells reservoirs. Propped fractures formed in the formation after fracturing are the key channels for shale gas production. Accurate evaluation of local propped fracture conductivity is of great significance to the effective development of shale gas. Due to the complex lithology and well-developed bedding of shale, the fracture surface morphology after fracturing is rougher than that of sandstone. This roughness will affect the placement of the proppant in the fracture and thus affect the conductivity. At present, fracture conductivity tests in laboratories are generally based on the standard/modified API/ISO method, ignoring the influence of fracture surface roughness. The inability to obtain the rock samples with the same rough morphology to carry out conductivity testing has always been a predicament in the experimental study on propped fracture conductivity. Herein, we propose a new method to reproduce the original fracture surface, and conductivity test samples with uniform surface morphology, consistent mechanical properties were produced. Then, we have carried out experimental research on shale-propped fracture conductivity. The results show that the fracture surfaces produced by the new method are basically the same as the original fracture surfaces, which fully meet the requirements of the conductivity test. The propped fracture conductivity is affected by proppant properties and fracture surface, especially at low proppant concentration. And increasing proppant concentration will help increase the predictability of conductivity. Due to the influence of the roughness of the fracture surface, there may be an optimal proppant concentration under a certain closure pressure.
Kazak, Ekaterina S. (Lomonosov Moscow State University) | Kazak, Andrey V. (Center for Hydrocarbon Recovery, Skolkovo Institute of Science and Technology) | Bilek, Felix (Dresden Groundwater Research Centre)
Summary In this study, we aim to develop a new integrated solution for determining the formation water content and salinity for petrophysical characterization. The workflow includes three core components: the evaporation method (EM) with isotopic analysis, analysis of aqueous extracts, and cation exchange capacity (CEC) study. The EM serves to quickly and accurately measure the contents of both free and loosely clay-bound water. The isotopic composition confirms the origin and genesis of the formation water. Chemical analysis of aqueous extracts gives the lower limit of sodium chloride (NaCl) salinity. The CEC describes rock-fluid interactions. The workflow is applicable for tight reservoir rock samples, including shales and source rocks. A representative collection of rock samples is formed based on the petrophysical interpretation of well logs from a complex source rock of the Bazhenov Formation (BF; Western Siberia, Russia). The EM employs the retort principle but delivers much more accurate and reliable results. The suite of auxiliary laboratory methods includes derivatography, Rock-Eval pyrolysis, and X-ray diffraction (XRD) analysis. Water extracts from the rock samples at natural humidity deliver a lower bound for mineralization (salinity) of formation water. Isotopic analysis of the evaporated water samples covered δO and δH. A modified alcoholic ammonium chloride [(NH4Cl)Alc] method provides the CEC and exchangeable cation concentration of the rock samples with low carbonate content. The studied rock samples had residual formation water up to 4.3 wt%, including free up to 3.9 wt% and loosely clay-bound water up to 0.96 wt%. The latter correlates well to the clay content. The estimated formation water salinity reached tens of grams per liter. At the same time, the isotopic composition confirmed the formation genesis at high depth and generally matched with that of the region's deep stratal waters. The content of chemically bound water reached 6.40 wt% and exceeded both free and loosely bound water contents. The analysis of isotopic composition proved the formation water origin. The CEC fell in the range of 1.5 to 4.73 cmol/kg and depended on the clay content. In this study, we take a qualitative step toward quantifying formation water in shale reservoirs. The research effort delivered an integrated workflow for reliable determination of formation water content, salinity lower bound, and water origin. The results fill the knowledge gaps in the petrophysical interpretation of well logs and general reservoir characterization and reserve estimation. The research novelty uses a unique suite of laboratory methods adapted for tight shale rocks holding less than 1 wt% of water.
Summary Reserves estimation is an essential part of developing any reservoir. Predicting the long-term production performance and estimated ultimate recovery (EUR) in unconventional wells has always been a challenge. Developing a reliable and accurate production forecast in the oil and gas industry is mandatory because it plays a crucial part in decision-making. Several methods are used to estimate EUR in the oil and gas industry, and each has its advantages and limitations. Decline curve analysis (DCA) is a traditional reserves estimation technique that is widely used to estimate EUR in conventional reservoirs. However, when it comes to unconventional reservoirs, traditional methods are frequently unreliable for predicting production trends for low-permeability plays. In recent years, many approaches have been developed to accommodate the high complexity of unconventional plays and establish reliable estimates of reserves. This paper provides a methodology to predict EUR for multistage hydraulically fractured horizontal wells that outperforms many current methods, incorporates completion data, and overcomes some of the limitations of using DCA or other traditional methods to forecast production. This new approach is introduced to predict EUR for multistage hydraulically fractured horizontal wells and is presented as a workflow consisting of production history matching and forecasting using DCA combined with artificial neural network (ANN) predictive models. The developed workflow combines production history data, forecasting using DCA models and completion data to enhance EUR predictions. The predictive models use ANN techniques to predict EUR given short early production history data (3 months to 2 years). The new approach was developed and tested using actual production and completion data from 989 multistage hydraulically fractured horizontal wells from four different formations. Sixteen models were developed (four models for each formation) varying in terms of input parameters, structure, and the production history data period it requires. The developed models showed high accuracy (correlation coefficients of 0.85 to 0.99) in predicting EUR given only 3 months to 2 years of production data. The developed models use production forecasts from different DCA models along with well completion data to improve EUR predictions. Using completion parameters in predicting EUR along with the typical DCA is a major addition provided by this study. The end product of this work is a comprehensive workflow to predict EUR that can be implemented in different formations by using well completion data along with early production history data.
We present a workflow to estimate recovery in unconventional reservoirs that uses flow simulation models constrained by seismic data, geomechanical parameters, and hydraulic stages properties. The goal of the workflow is the rapid testing of different hydraulic stage scenarios in the presence of natural fractures and other hypotheses that can be compared to select the one that yields optimal recovery. All the parameters of interest are generated directly into a flow simulation grid centered on the horizontal well. Thickness of hydraulic stages equals that of one cell of the simulation grid and therefore, details of individual hydraulic fractures are not explicitly considered allowing modeling of larger reservoir scale effects on recovery. The first step is the estimation of natural fracture orientations using seismic data calibrated with independent fracture information. Then, the flow grid is also populated with geomechanical parameters such as stress field and stress orientations, pore pressure, and friction coefficient. After defining locations and geometry of hydraulic stages along the well path and assuming fluid pressure decay functions away from the hydraulic stages, we use Mohr-Coulomb faulting theory to estimate which natural fractures are more prone to reactivation after hydraulic stimulation. This volume of reactivated natural factures is then upscaled to effective fracture permeability that serves as input to an ultra-fast dual-permeability flow simulator. Finally, once the model is in the flow simulator, we use fluid properties and other dynamic parameters for calibrating with production information, changing the simulation model if needed, and performing long term forecast. We illustrate the application of the workflow in the Eagle Ford formation (South Texas) using a data set that consists of 3D seismic, outcrop descriptions, geomechanics measurements, and production information.
Unconventional reservoirs are characterized by extremely low permeabilities that hinder fluid communication between the reservoir and the borehole. These permeabilities are enhanced by the generation of hydraulic fractures after high-pressure fluid is injected into the formations of interest. Even though hydraulic fractures are the main source of permeability enhancement near the wellbore, reactivation of existing natural fractures in the vicinity of the hydraulic fractures is also an important mechanism of self-propped permeability enhancement in the stimulated reservoir volume (SRV) (Gutierrez et al., 2000; Zhang and Li, 2016; Rutledge and Phillips, 2003) and the hydraulic fractures regions (Jeffrey, 2010; Maxwell, 2011).
Reactivation of existing natural fractures depends on the current state of stress, orientation and intensity of existing natural fractures relative to the stress field, injected fluid pressures, rock properties, and geometry of hydraulic stages. In this paper, we consider all these parameters in an integrated fashion that uses Mohr-Coulomb faulting theory to estimate the likelihood of slip of existing natural fractures. Then, we use simple aperture versus fluid pressure assumptions to generate effective permeability volumes of reactivated fractures.
Tran, Ngoc Lam (University of Oklahoma) | Gupta, Ishank (University of Oklahoma) | Devegowda, Deepak (University of Oklahoma) | Jayaram, Vikram (Pioneer Natural Resources) | Karami, Hamidreza (University of Oklahoma) | Rai, Chandra (University of Oklahoma) | Sondergeld, Carl H. (University of Oklahoma)
Summary In this study, we demonstrate the application of an interpretable (or explainable) machine‐learning workflow using surface drilling data to identify fracturable, brittle, and productive rock intervals along horizontal laterals in the Marcellus Shale. The results are supported by a thorough model‐agnostic interpretation of the input/output relationships to make the model explainable to users. The methodology described here can easily be generalized to real‐time processing of surface drilling data for optimal landing of laterals, placing of fracture stages, optimizing production, and minimizing fracture hits. In practice, this information is rarely available in real time and requires tedious and time‐consuming processing of logs (including image logs), core, microseismic data, and fiber‐optic‐sensor data to provide post‐job validation of fracture and well placement. Post‐completion analyses are generally too late for corrective action, leading to wells with a low probability of success and increasing risk of fracture hits. Our workflow involves identifying geomechanical facies from core and well‐log data. We verify that the geomechanical facies derived using core and well‐log data have characteristically different brittleness, fracturability, and production characteristics. We test and investigate several different supervised classifiers to relate surface drilling data to the geomechanical facies. The data were divided into training and test data sets, with supervised classification techniques being able to accurately predict the geomechanical facies with 75% accuracy on the test data set. The clusters predicted on test well (unseen data) were qualitatively verified using the microseismic interpretation. The use of Shapley additive explanations (SHAP) helps explain the predictive models, rank the importance of various inputs in the prediction of the facies, and provides both local and global sensitivities. Our study demonstrates that pre‐existing natural‐fracture networks control both the hydraulic‐fracture geometry as well as the production. Natural fractures promote the formation of complex fracture networks with shorter half‐lengths, which increase well productivity while minimizing fracture hits and neighboring‐well interactions. The natural‐fracture network is itself controlled by the geomechanical properties of the rock. The ability of the surface drilling data to reliably predict the geomechanical rock facies provides a powerful tool for real‐time optimization of wellbore trajectory and completions.
In this study, we conduct two-dimensional hydraulic fracture (HF) simulations using Finite-Discrete Element Method (FDEM) in naturally fractured media with different matrix permeability and natural fracture density. Natural fractures (NFs) and fluid flow through the porous matrix and fractures are explicitly modeled in this framework through a fully coupled hydromechanical formulation. The stress redistribution due to the presence of discrete fracture network (DFN) and the complex pattern of HF propagation path due to HF/NF interactions are captured in these numerical simulations. For validation, the results of two-dimensional and hydromechanical FDEM simulations are compared to laboratory experiments and analytical solutions for hydraulic fracture initiation and propagation from a notch on a pressurized cavity in an impermeable and homogeneous medium under an anisotropic stress condition. Results of simulations reveal the significant role of NF pattern and permeability of the rock matrix on its response to HF stimulation. Hydraulic treatment of a medium with denser DFN activates more NFs and will more likely create flow channeling through some of the surrounding NFs. Size of the wet stimulated reservoir area depends on the permeability of the rock matrix, but the size of dry stimulated reservoir area is independent of the permeability.
Hydraulic fracturing technology has brought us a lot of economic and societal benefits because it makes the extraction of oil, gas, and heat from low permeability rocks possible. However, the accurate design of efficient well treatment operations to create sustainable stimulated reservoir volume (SRV) is not possible yet. Commonly used simplified models (linear elastic fracture mechanics integrated with lubrication theory) cannot predict the behavior of natural reservoirs because those models are developed for linear, elastic, homogeneous, isotropic intact rocks filled with Newtonian fluids. Natural rocks are Discontinuous, Inhomogeneous, Anisotropic, and Non-Elastic (DIANE) materials (Harrison and Hudson, 2000). In addition, they are porous and permeable, thus a complex set of poro-mechanical properties influence their behavior. Hydraulic fracturing, therefore, involves multiple interacting phases (rock blocks, granular materials, and fluids), and the behavior vary drastically depending on the involved scales, in-situ state of stress, host fluid properties, treatment parameters (e.g., viscosity and flow rate), poromechanical properties of the rock matrix, morphology, size, spacing, pattern, mineralization of the natural fractures (NF), and their relative orientation with respect to the wellbore and present-day principal stresses (Blanton, 1982; Warpinski and Teufel, 1987; Gale, et al., 2014; Raterman, et al., 2018; Daneshy, 2019).
Chan, S. A (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals) | Hassan, A. M (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals) | Humphrey, J. D (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals) | Mahmoud, M. A (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals) | Abdulraheem, A. (College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals)
In this study, we applied machine learning approach to estimate the mineralogical compositions based on elemental data acquired using x-ray fluorescence (XRF) instruments. Artificial neural networks (ANN) was used to develop new models to provide continues profiles of quartz, calcite, and clay minerals using profiles of Na, Al, Si, K, and Ca. Thereafter, the mineral-based brittleness index (MBI) was estimated using the predicted profiles of quartz, calcite, and clay minerals. The obtained results showed that the developed models can provide accurate predictions for the mineralogical profiles and brittleness index, with R2 of around 0.96. Finally, new empirical correlations were extracted from the ANN models, which can provide accurate and quick estimations for the mineralogical composition. The ANN-based equations were validated using testing data; very acceptable performance was obtained with R2 higher than 0.95.
This work therefore will have benefit in obtaining high-resolution mineralogy using XRF data without sending many samples for XRD laboratory measurements. Eventually, the predicted mineralogy can be used to quantify the BI and enable accurate predictions that result in better de-risking strategies and evaluating successful unconventional plays in terms of estimations of source-rock quality, identification of sweet spots, and designing/executing well placement and hydraulic fracturing stages.
Because of low porosity and insufficient permeability to allow the hydrocarbons flow naturally, hydrocarbons within organic-rich mudstones or referred to as "unconventional” typically extracted by a combination of vertical and horizontal drilling followed by multi-staged hydraulic fracturing. Fracking will increase effective permeability and allows the hydrocarbon to be released and economically produced (Lee et al., 2011; Sone and Zoback, 2013; Dong et al., 2017). Several key parameters were used to evaluate sweet spot as well as designing drilling, completion and stimulation parameters. Therefore, it is important to identify rock compositions and understand the factors affecting their properties, known as brittleness index (BI).
Wang, Lipeng (Schlumberger) | Du, Xianfei (PetroChina) | Qiu, Kaibin (Schlumberger) | Wu, Shunlin (PetroChina) | Zhuang, Xiangqi (Schlumberger) | Bai, Xiaohu (PetroChina) | Wang, Lizhi (Schlumberger) | Pan, Yuanwei (Schlumberger)
Severe frac hit incidents were encountered during stimulating horizontal production wells in a pilot pad of a tight oil reservoir in Ordos basin. The reservoir is interbedded, highly heterogeneous both vertically and horizontally as result of gravity flow deposition. This raised a big concern on the stimulation treatment size and well spacing of the pad and further implication on the field development plan of the reservoir. A deep understanding of root causes and an effective frac hit prevention and mitigation strategy were much needed to address the problem.
A multidisciplinary team was formed to investigate the problem. Initial analysis on the frac hit incidents showed that the frac hits were not correlated to well spacing, or stimulation volume as frac hits only occurred at certain stages out of those with the same stimulation volume or well spacing. Then the study turned the focus to investigating the problem from the geological and geomechanical perspective through leveraging a 3D high definition (HD) geological and geomechanical model.
The detailed investigation revealed that the root causes of the frac hits were multiple folds: Existence of natural fracture corridors led to frac hit of wells in much longer distance. Alignment of hydraulic fractures among horizontal wells also increased the chance of frac hits as the result of alignment of perforations among horizontal wells. Asymmetrical propagation of hydraulic fracture (e.g., deviated to one side of horizontal laterals increased the chance of frac hit as the result of heterogeneous sand body and in-situ stress field.) Lack of vertical fracture containment resulted in the crossing-layer frac hits.
Existence of natural fracture corridors led to frac hit of wells in much longer distance.
Alignment of hydraulic fractures among horizontal wells also increased the chance of frac hits as the result of alignment of perforations among horizontal wells.
Asymmetrical propagation of hydraulic fracture (e.g., deviated to one side of horizontal laterals increased the chance of frac hit as the result of heterogeneous sand body and in-situ stress field.)
Lack of vertical fracture containment resulted in the crossing-layer frac hits.
With the good understanding of the root causes of frac hits, a frac hit risk map was generated based on the 3D HD geoscience model, which highlighted high frac risk zone in the pad. The frac hits observed from subsequent stimulations of the horizontal wells occurred only in the high frac hit risk zone, which validated the frac hit risk map. Based on the findings from the study, the frac hit prevention and mitigation strategy was developed.
He, Youwei (Southwest Petroleum University) | Guo, Jianchun (Southwest Petroleum University) | Tang, Yong (Southwest Petroleum University) | Xu, Jianliang (Geological Exploration and Development Research Institute, CNPC Chuanqing Drilling Engineering Co. Ltd.) | Li, Yanchao (Shale Gas Exploration & Development Project Department, CNPC Chuanqing Drilling Engineering Co. Ltd.) | Wang, Yong (Geological Exploration and Development Research Institute, CNPC Chuanqing Drilling Engineering Co. Ltd.) | Lu, Qianli (Southwest Petroleum University) | Patil, Shirish (King Fahd University of Petroleum & Minerals) | Rui, Zhenhua (Massachusetts Institute of Technology) | Sepehrnoori, Kamy (The University of Texas at Austin)
Severe fracturing interference in multi-well pads has been identified in shale gas reservoirs. The gas production of affected multi-fractured horizontal wells (MFHWs) decrease a lot and is hard to restore for most wells even after fracturing fluid flowback. Currently, well interference caused by fracturing operations has become the most important factor affecting the shale gas production. However, the mechanism of fracturing interference and its quantitative impact on gas production in shale gas reservoir are not clear.
The aim of this work is to assess the mechanism and dominated factors of fracturing interference of multi-well pads in shale gas reservoirs, and evaluate the impact of interwell fracturing interference on shale gas production. Firstly, field data in WY Basin are applied to calculate the ratio of impacted wells to newly fractured wells and understand the influencing degree and recovering degree of gas production. The main controlling factors of fracturing interference are determined and the interwell fracturing interacting types are presented. Furthermore, the production recovering potential for impacted wells are analyzed. Finally, some suggestions for mitigating fracturing interference are provided.
The impact degree and recovering degree of gas production are divided into three categories. The dominated factors of fracturing interference include well spacing, pressure of the affected wells before interference, gas production before interference, and flowback ratio of fracturing fluid. The influencing degree of gas production can be estimated by using the generated equations of impact degree of gas production per well spacing (IDGPs) or impact degree of gas production per flowback ratio (IDGPf) Another novel finding is that 70% of affected parent wells belong to adjacent well pad compared with the newly fractured child well. The interwell fracturing interference is divided into four types, including pressure interference without direct communications between two MFHWs (Type I), fracturing interference through natural fracture/secondary fractures (Type II), fracturing interference through hydraulic fractures (Type III), and direct communication between hydraulic fractures and wellbore of adjacent well (Type IV). Fracturing communication through hydraulic fractures or secondary/natural fractures are more common, and the impact on well safety and production performance increases from Type I to Type IV. Therefore, the fracturing parameters need to be optimized to reduce the fracturing interference. This study can provide reasonable suggestions for infill well optimization, fracturing design, and interwell fracturing interference mitigation to achieve the highest gas recovery of all multi-well pads in shale gas reservoirs.
Dong, Xiaohu (China University of Petroleum, Beijing) | Luo, Qilan (China University of Petroleum, Beijing) | Wang, Jing (China University of Petroleum, Beijing) | Liu, Huiqing (China University of Petroleum, Beijing) | Chen, Zhangxin (University of Calgary) | Xu, Jinze (University of Calgary) | Zhang, Ge (Sinopec Shengli Oilfield)
Nanopores in tight and shale reservoirs have been confirmed by numerous studies. The nanopores are not only the primary storage space of oil and gas, but also the main transport channels of confined fluids. Although considerable efforts have been devoted to study the confined behavior of hydrocarbon fluids in nanopores, most of them have a local smooth-surface assumption. The effect of pore heterogeneity is still lacking. In this paper, in order to effectively simulate the nanopore complexity, we propose the assumptions of furrowed surface and sinusoidal surface to represent the heterogeneous nanopores (or rough nanopores) in tight and shale rocks. Then, based on these assumptions, the multicomponent potential theory of adsorption (MPTA) is coupled with the Peng-Robinson equation of state (PR EOS) to investigate the behavior of hydrocarbon fluids in rough nanopores. In this theory, considering the different types of nanopore heterogeneity, the geometrical heterogeneity is modeled by a spatial deformation of the potential field, and the chemical heterogeneity is modeled by an amplitude deformation of this field. The fluid-fluid interactions are modeled by the PR EOS, and the fluid-surface interactions are modeled by a Steel 10-4-3 potential for slit-like nanopres and a modified Lennard-Jones (LJ) 12-6 potential for cylindrical nanopores. Then a prediction process for the behavior of methane, ethane, propane and their mixtures is performed. The results are compared against the experimental data of their adsorption isotherms from publishd literatures to validate the accuracy of the theory and process. Then, the effect of pore heterogeneity on the confined behavior of methane, ethane, propane is quantitatively studied.
Results indicate that for the experimental data considered in this work, the theory for heterogeneous nanopores is capable of predicting the confined behavior of hydrocarbons in a wide range of pressure and temperature. The developed mathematical model can well predict the confined behavior of fluids both in slit-like and cylindrical nanopores. Compared with the results of a smooth pore surface, the geometrical heterogeneity can significantly affect the thermodynamic properties of hydrocarbon fluids, but the chemical heterogeneity cannot strongly distort the confined behavior of fluids. The effect of geometrical heterogeneity on the confined behavior of fluids mainly depends on the effective pore size. In hydrocarbon fluids, as the composition of heavy components increase, the effect of heterogeneity on the confined behavior of fluids is reduced. Also, as the nanopore size reduces, the effect of pore heterogeneity on the confined behavior of fluids is enhanced. For fluid mixture, compared with smooth surfaces, it is observed that for heterogeneous surface, the mole fraction of the heavy component in the vicinity of pore wall can increase significantly, and that of the light component is reduced. This investigation makes it possible to completely characterize the confined behavior of a confined fluid in heterogeneous nanopores.