Unconventional shale resources are drilled horizontally following the geologic bed dip upward or downward and completed with multi-stage fracturing to maximize reservoir contact. A recent study in Oklahoma claimed that toe-up (inclined upward) laterals yield the highest production rate and estimated ultimate recovery. The objective of this study is to investigate drilling fluid hydraulics and well control operations in toe-up laterals and compare the results to toe-down laterals.
This study uses a multiphase steady-state hydraulics and a dynamic well control simulator. A hydraulics model was developed and verified with a data from a recently drilled Marcellus shale lateral in Monongalia County, WV in 2015. Static and dynamic pressure profiles were examined at drilling flow rate and at slow pump rate. Further, this research studies gas kick behavior and well control practices for kicks experienced at shallow, middle, and deep zones in the lateral section. Additionally, it considers the impact of operational parameters and influx characteristics on wellbore integrity.
The results of this study showed that the developed hydraulics model successfully predicted the pump pressure with an accuracy of 0.97. Larger kick sizes result in higher pit gain, gas flow rate, choke, and casing shoe pressures in toe-down laterals. In contrast, in toe-up laterals, the higher the kick size, the longer the circulation times with an insignificant impact on the choke and shoe pressures. In toe-up laterals, gas bubbles migrated towards the toe and accumulated in high side pockets. Likewise, choke experienced less pressure, volume, and gas discharge rate for extended periods of time in toe-up laterals. Therefore, higher circulation rates and longer circulation times were essential to flush out the dispersed and trapped gas bubbles. The closer the kick location to the vertical section of the well, the shorter the circulation time needed. However, kicks experienced at the heel resulted in higher pit gain, gas discharge rate, choke pressure, and consequently high casing shoe pressure.
Identifying the consequences of drilling toe-up laterals on hydraulics and well control is crucial for drilling operations. This improves rig and personnel safety and reduces the blowout associated risks. Accordingly, it is critical to verify the well integrity by monitoring surface choke, casing shoe, and constant bottomhole pressures throughout the entire well control operations.
The production from a hydraulically fractured unconventional well depends on the stimulated permeability and its interaction with the naturally fractured background permeability. Since the propagation of a hydraulic fracture is often asymmetric and depends on geomechanical factors, the ensuing pressure depletion and the EUR depends on this asymmetric behavior. An analytical asymmetric tri-linear model to approximate pressure depletion is presented. The model uses asymmetric frac design results as input and estimates the pressure depletion around a parent well. This new approach represents an acceptable alternative to full reservoir simulation when investigating frac hits problems.
This asymmetric tri-linear model was combined with our poro-elastic geomechanical modeling simulator in order to capture the physics created by the depleted pressure sink zone. This physics combines the stimulation operations in the neighboring infill well and their interactions with the complex local and far scale geologic features such as natural fractures and faults.
The pressure depletion determined at an Eagle Ford well using the asymmetric tri-linear model was similar to those found with a full reservoir simulator. Hydraulic fracture modeling of a child well located in the vicinity of a parent well with a pressure depleted zone highlighted the potential of developing a frac hit if geological features in the area were creating fluid and pressure conduits. A similar observation is made for a Wolfcamp well where a fault affected the nearby stage causing interference between potential stacked wells.
The integration of the asymmetric tri-linear model and our geomechanical simulator presents the necessary completion modeling tool to quickly, yet accurately design hydraulic fracturing while preventing frac hits, especially now with the increasing of number of infill unconventional wells.
A method is developed to detect the precursors of drilling events based on drilling data such as surface data, wellbore geometry data, lithology (formation characteristics), and downhole measurements from various downhole tools. The drilling events refer to interesting behavior of the drilling system detected or recorded, such as severe vibration, stuck pipe, fluid loss, sudden equivalent circulating density (ECD) changes, etc.
The method is based on various machine learning techniques to learn the changing trend of drilling parameters when the drilling events happen. Specifically, the drilling events are first extracted from massive drilling data using defined thresholds and/or criteria. Then, the time series of drilling parameters are represented by symbolic aggregate approximation (SAX). The patterns of these SAX strings are clustered by unsupervised learning and then used for pattern recognition with dynamic time warping (DTW). Finally, the searching pattern recognition is proposed to classify the changing trend of drilling parameters.
The traditional SAX method is not suitable for drilling data processing because it assumes that the data should be Gaussian distributed. With the modified SAX, two sets of SAX parameters are used to cluster the time series by unsupervised learning with dynamic time warping distance as measure. It is found that one set of SAX parameters (alphabet size of 5 and 15-data-point window) could yield more reasonable patterns, including flat, ramp up, ramp down, step up, step down, pulse up, and pulse down. The changing trends of drilling parameters are classified to the predefined patterns with shortest DTW distance by using searching pattern recognition method. Finally, several experiments are conducted to demonstrate the effectiveness of proposed method.
This is an innovative work to apply the data mining and machine learning techniques to drilling interpretation. It provides a useful way for the remote center to monitor the onset of abnormal drilling events and inform the drillers taking actions to optimize the drilling operation. In addition, it helps the drilling community to understand the triggers of challenging drilling events such as stick/slip, whirling, etc.
In this paper, an integrated analysis and design method is presented to understand and quantify the effect of particle jamming near the entrance of perforation/wormhole tunnel. An advanced wellbore-scale three dimensional numerical studies with a coupled computational fluid dynamics (CFD) and discrete element model (DEM) were performed to simulate different mechanisms involved in particulate diversion. The results of wellbore-scale simulation were translated into an engineering particulate diversion model, based on the proven diversion mechanisms from laboratory and simulation. The model is incorporated into an integrated carbonate acidizing simulator.
Generally particulate diversion is not used in carbonate acidizing because of the formation of the wormholes and potential difficulty in removing particles from the induced wormholes or perforation tunnel. The new degradable particulate system addresses the issue and presents an efficient approach to divert acid in carbonate stimulation. Detailed physics based simulation demonstrate that the induced wormholes or perforation would plug thru two distinct mechanisms: (1) temporarily seal the entrance of small scale wormholes or perforation with a combination of small and large particles, and/or (2) large particles bridge along tapered path of wormhole/perforation and forms a temporary filter cake on the mouth of opening. Either of these diversion mechanisms will decrease the injectivity locally and promote fluid diversion from inside of well into other normally under-stimulated locations.
The integrated simulator is used to optimize acid stimulation of a vertical wellbore and explain the impact of operational parameters and subsurface conditions on the stimulation efficiency. The model results showed that most optimized bullheaded treatment can be significantly improved by utilizing the particulate diversion system. It is shown that that the developed skin from jammed particulate provided considerable diversion. The results also demonstrated the relation between treatment pressure, the quality of diversion, and subsurface conditions (e.g. permeability, porosity, reservoir pressure and temperature).
Even though the advances in horizontal drilling and hydraulic fracturing techniques have unlocked the gas contained in Marcellus shale, the quantification of the petrophysical properties remain challenging due to complex nature of the shale. Shale permeability is commonly measured by the unsteady state methods, such as pulse-decay or GRI methods, because the shale has a permeability in nano-Darcy range. The permeability values by determined by these techniques have been found often to have large margin of uncertainty as a result of inconsistent experimental protocols and the complex interpretations methods.
In this study, petrophysical properties of the Marcellus shale core plugs were measured using an innovative laboratory setup, referred to as Precision Petrophysical Analysis Laboratory (PPAL). PPAL is designed to accurately measure the petrophysical properties of ultra-low permeability core plugs under the reservoir conditions. PPAL measurements are performed under steady-state isothermal conditions flow conditions and the analysis of the results do not require complicated interpretations. The key advantage of the PPAL is the capability to measure the permeability and porosity of the shale core plugs under a wide range of confining and pore pressures. In addition, the impact of gas adsorption (or desorption) on the measurements can be monitored. The core plugs used in this study were made available through the Marcellus Shale Energy and Environment Laboratory (MSEEL), a dedicated field laboratory in the Marcellus Shale. MSEEL has been established to undertake field and laboratory research to advance and demonstrate new subsurface technologies and to enable surface environmental studies related to unconventional energy development. The filed site is owned and operated by Northeast Natural Energy, LLC and contains several horizontal Marcellus Shale wells. In addition, a vertical well has been drilled specifically for obtaining core, log, and other data for scientific purposes (science well).
The results of the core plug permeability measurements indicated that that the permeability values decline as the gas (pore) pressure increases. Reliable values of the absolute permeability can be obtained by the application of the double-slippage correction for all pore pressure ranges but more specifically for pore pressures below 900 psia. Klinkenberg correction on the other hand, can only provide reliable values for the absolute permeability when the pore pressures are above 900 psia. The determined absolute permeability values were found to be impacted by the net stress. The analysis stress data with the aid of Walsh plot provided the estimates of the fracture (fissure) closure pressure. The closure pressure was found to be dependent on the absolute permeability.
Volume and salt concentrations in Marcellus flowback water depend on geology, drilling and completions, stimulation and flowback operations. Recent studies include evaluations of geochemical origins based on the compostition concentrations, flowback sampling analysis and numerical studies. However, an in-depth understanding of chemical compositions as well as the changes of compositions is still needed.
In this paper, we will first review the literature related to flowback water in Marcellus shale gas wells to fully understand the chemistry, geochemistry, and physics governing a fracture treatment, shut-in, and flowback. We will then gather all public and in-house flowback data, named as 3-week or 3-month flowback in this work, to build a data set of flowback water compositions. After data screening, we will then analyze this composition database using four different methods: geographical, changes over time, linear regression, clustering and multi-variable analysis. New understandings such as the magnitude and prevailing trends of concentrations for target constituents as well as the correlations among flowback compositions, the differentiation between early and late time flowback water were obtained and explained on the basis of geochemistry and physics. Guidelines for a comprehensive sampling protocol will be provided based on our analysis.
Recent drilling and hydraulic fracturing activities in the Utica-Point Pleasant shale play have recorded total measured depth of over 27,000 feet a record for the longest onshore well in the United States (
One immediate solution to the current low energy prices is optimizing well spacing to enhance hydrocarbon recovery and, thus, the commercial feasibility of the project. Horizontal well spacing constitutes a fundamental parameter for the success of a shale-drilling venture. Determining the optimum horizontal well spacing in shale reservoirs represents a challenging task because of the complexity of controlling factors. These factors can be categorized into three groups: geological, engineering, and economic.
Geological modeling and reservoir simulation are the standard tools utilized in the industry to integrate these controlling factors. In this study, we employed these tools to perform sensitivity analysis of reservoir characteristics and future production optimization for a deep drilling case study in the Utica-Point Pleasant formations. We sought to find the optimum horizontal well spacing scenario as well as hydraulic fracturing design, in order to attain the highest net present value (NPV) for 50 years of gas production. Our reservoir model represented a portion of Utica-Point Pleasant formations at the depth of 13,000 feet and the dry gas window. A commercial reservoir simulator was coupled with an optimization algorithm to reach the best solution with a minimum simulation cost. In addition, we developed a smart proxy based on artificial neural networks (ANNs) for fast analysis of estimated ultimate recovery (EUR) and NPV. Although the outcome of our study is subjective to the chosen asset, the workflow provides a good example of horizontal well spacing and hydraulic fracturing design optimization as well as a simple and fast technology to predict the critical parameters.
In-situ stresses and heterogeneity of the formation rock are the dominant factors that influence hydraulic fracturing process. The rheology of frac fluid also significantly affects hydraulic fracturing treatment regarding fracture propagation, proppant transport, formation property alteration, and flow back process. Non-Newtonian CO2 foams stabilized by nanoparticles has been recently studied as a promising frac fluid, which is advanced in less water contents, proppant placement, fast clean, and maintaining conductive channels. For the application of the new frac fluid for unconventional reservoir development, it is critical to investigate fracture propagation and proppant transport using CO2 foams.
This study simulated hydraulic fracturing and proppant transport by viscous gas foams in a horizontal well perforated in Eagle Ford Shale formation of Zavala County, TX. A 3D numerical model was setup with heterogeneous reservoir properties using a commercial fracturing simulator - GOHFER. To represent formation heterogeneity, the rock mechanical properties were derived from well logs including Gamma Ray, Resistivity, Neutron Porosity, and Density Porosity logs, characterized by Young's modulus (3 × 106 ~ 6 × 106 psi), Biot constant (0.6 ~ 0.8), and Poisson's ratio (0.2 ~ 0.4). The flow behavior of CO2 foams stabilized by nanoparticles was characterized by Carreau rhelogy model based on the experimental data. During the pumping schedule for multistage fracturing process, the effects of variable injection rate (20 ~ 40 BPM), CO2 foams quality (50 ~ 80 %), incremental proppant distribution (0 ~ 5 PPA), and fluid leakoff were investigated.
The results showed that a laterally un-even shape of fracture propagation profile was developed during multi-stage CO2 fracturing, which represents the reservoir heterogeneity with varying in-situ stress and poroelastic properties. The results also indicated that the rheology of frac fluid significantly influences the fractures propagation. As the viscosity of CO2 foams increases with variation of foam quality from 50% - 80%, fracture width increases but fracture length decreases. The fluid loss during fracturing was quantified by pressure dependent leakoff approach. For different CO2 foams quality (50% - 80%), fluid leakoff rate decreases with increasing the CO2 foam quality.
This study provides a pioneering insight and improved fracture treatments design by non-Newtonian frac fluid - CO2 foams application with increased fracture conductivity and efficiency, which is vital for hydrocarbon exploitation from unconventional reservoirs.
Microseismicity is a physical phenomenon which allows us to estimate the production capability of the well after hydraulic fracturing (HF) in a naturally fractured (NF) reservoir. Some of the microseismic events are reactivations of NFs induced by a direct hit of HF, while others are induced by the fluid leak-off from the previous stages or by elastic waves emitted into the reservoir with hydraulic fracture plane propagation. The former NFs have a chance to be propped there as the latter will not significantly increase their contribution to the production. Identification of such microseismic events helps to reduce uncertainty in the description of fracture network geometry.
Based on inferred data from core analysis NF densities and orientations, we generated multiple realizations of the semi-stochastic Discrete Fracture Network (DFN). In order to constrain them, we used time evolution of microseismic cloud in addition to results of core analysis. Fluid and proppant pumping schedule is used to identify such microseismic events because they should be located close to the pressure diffusion front generated by hydraulic fluid. Events outside of proposed region may be triggered by other factors, such as stress-strain relaxation from other stages and correspondent fractures. In most cases, they are not wide enough to take proppant from the main HF. This approach was used to reduce range of production for DFN realizations.
This workflow is implanted to a 15-stage hydraulic fracture treatment on a horizontal well placed in a siltstone reservoir with intrinsic fractures. The spatio-temporal dynamics of microseismic events are classified into two groups by the front of nonlinear pressure diffusion caused by 3-dimensional hydraulic fracturing, considered as effective and ineffective events. DFNs with only effective microseismicity and with all the induced events are generated. Then, two types of DFN related uncertainties on production are performed to evaluate the impact of filtration. Results of aleatory uncertainty quantification caused by the randomness of DFN modeling indicate the filtered events can generate a production DFN with a more consistent connected fracture area. Moreover, sensitivity analysis caused by lack of accuracy in natural fracture characterization shows the production area of DFN with filtration process is more insensitive to the variation of fracture parameters. Finally, a history match with production data and pressure data indicates this DFN model properly represents the reservoir and completion.
Our methodology characterizes well the conductive fracture network utilizing core data, microseismic data, and pumping schedule. It could restore the true productivity of each fractured stage from a massive microseismic cloud, which helps understand the contribution of fracturing job right after the treatment.
Performing a reservoir simulation study for hydraulically fractured horizontal wells in unconventional reservoirs relies on input parameters which are not often well defined. The uncertainty of the input parameters (i.e. completion design, petrophysics, reservoir fluid phase) leads to uncertainty in the resulting history matches and less confidence when using the model results. This paper focuses on the importance of fluid phase characterization in reservoir simulation studies.
One of the challenges the industry currently faces is PVT (pressure-volume-temperature) fluid characterization for tight rock formations. When submitting a production fluid sample for analysis, it is crucial to define an accurate estimate of pressure, temperature, and gas-oil ratio (GOR) in order to place the sample in the appropriate fluid window to yield a representative PVT characterization for use in reservoir simulation studies.
The case study presented in this paper describes a reservoir simulation study in the Powder River Basin with varying fluid regimes across the field (