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Abstract Market-induced production shut-downs and restarts offer us an opportunity to gather step-rate and shut-in data for pressure transient analysis (PTA) and rate transient analysis (RTA). In this study, we present a unified transient analysis (UTA) to combine PTA and RTA in a single framework. In this new approach continuous production data, step-rate data, shut-in data and re-start data can be visualized and analyzed in a single superposition plot, which can be used to estimate both and infer formation pore pressure in a holistic manner by utilizing all available data. Most importantly, we show that traditional log-log and square root of time plots can lead to false interpretation of the termination of linear-flow or power-law behavior. Field cases are presented to demonstrate the superiority of the newly introduced superposition plot, along with discussion on the calibration of long-term bottom-hole pressure with short-term measurements.
Guo, Yifei (The University of Texas at Austin) | Ashok, Pradeepkumar (The University of Texas at Austin) | van Oort, Eric (The University of Texas at Austin) | Patterson, Ross (Hess Corporation) | Zheng, Dandan (Hess Corporation) | Isbell, Matthew (Hess Corporation) | Riopelle, Austin (Marathon Oil Corporation)
Abstract Well interference, which is commonly referred to as frac hits, has become a significant factor affecting production in fractured horizontal shale wells with the increase in infill drilling in recent years. Today, there is still no clear understanding on how frac hits affect production. This paper aims to develop a process to automatically identify the different types of frac hits and to determine the effect of stage-to-well distance and frac hit intensity on long-term parent well production. First, child well completions data and parent well pressure data are processed by a frac hit detection algorithm to automatically identify different frac hit intensities and duration within each stage. This algorithm classifies frac hits based on the magnitude of the differential pressure spikes. The frac stage to parent well distance is also calculated. Then, we compare the daily production trend before and after the frac hits to determine the severity of its influence on production. Finally, any evident correlations between the stage-to-well distance, frac hit intensity and production change are identified and investigated. This work utilizes 3 datasets covering 22 horizontal wells in the Bakken Formation and 37 horizontal wells in the Eagle Ford Shale Formation. These sets included well trajectories, child well completions data, parent well pressure data and parent well production data. The frac hit detection algorithm developed can accurately detect frac hits in the available dataset with minimal false alerts. The data analysis results show that frac hit severity (production response) and intensity (pressure response) are not only affected by the distance between parent and child wells, but also affected by the directionality of the wells. Parent wells tend to experience more frac hits from the child frac stages with smaller direction angles and shorter stage-to-parent distances. Formation stress change with time is another factor that affects frac hit intensity. Depleted wells are more susceptible to frac hits even if they are further from the child wells. Also, we observe frac hits in parent wells due to a stimulation of a child well in a different shale formation. This paper presents a novel automated frac hit detection algorithm to quickly identify different types of frac hits. This paper also presents a novel way of carrying out production analysis to determine whether frac hits in a well have positive or negative influence long-term production. Additionally, the paper introduces the concept of the stage-to-well distance as a more accurate metric for analyzing the influence of frac hits on production.
Abstract As our industry is tapping into tighter carbonate reservoirs than in the past, completion techniques need to be improved to stimulate the low-permeability carbonate formation. Multistage acid fracturing technique has been developed in recent years and proved to be successful in some carbonate reservoirs. A multistage acid fracturing job is to perform several stages of acid fracturing along a horizontal well. The goal of acid fracturing operations is to create enough fracture roughness through differential acid etching on fracture walls such that the acid fracture can keep open and sustain a high enough acid fracture conductivity under the closure stress. In multistage acid fracturing treatments, acid flow is in a radial flow scenario and the acid etching process can be different from acid fracturing in vertical wells. In order to accurately predict the acid-fracture conductivity, a detailed description of the rough acid-fracture surfaces is required. In this paper, we developed a 3D acid transport model to compute the geometry of acid fracture for multistage acid fracturing treatments. The developed model couples the acid fluid flow, reactive transport and rock dissolution in the fracture. We also included acid viscous fingering in our model since viscous fingering mechanism is commonly applied in multistage acid fracturing to achieve non-uniform acid etching. Our simulation results reproduced the acid viscous fingering phenomenon observed from experiments in the literature. During the process of acid viscous fingering, high-conductivity channels developed in the fingering regions. We modeled the acid etching process in multistage acid fracturing treatments and compared it with acid fracturing treatments in vertical wells. We found that due to the radial flow effect, it is more difficult to achieve non-uniform acid etching in multistage acid fracturing treatments than in vertical wells. We investigated the effects of perforation design and pad fluid viscosity on multistage acid fracturing treatments. We need to have an adequate number of perforations in order to develop non-uniform acid etching. We found that a higher viscosity pad fluid helps acid to penetrate deeper in the fracture and result in a longer and narrower etched channel.
Koparal, Gulcan Bahar (The University of Texas at Austin, Turkish Petroleum) | Sharma, Himanshu (The University of Texas at Austin, Indian Institute of Technology Kanpur) | Liyanage, Pathma J. (The University of Texas at Austin,Ultimate EOR Services) | Panthi, Krishna K. (The University of Texas at Austin) | Mohanty, Kishore (The University of Texas at Austin)
Abstract High surfactant adsorption remains a bottleneck for a field-wide implementation of surfactant floods. Although alkali addition lowers surfactant adsorption, alkali also introduces many complexities. In our systematic study, we investigated a simple and cost effective method to lower surfactant adsorption in sandstones without adding unnecessary complexities. Static and dynamic surfactant adsorption studies were conducted to understand the role of sacrificial agent sodium polyacrylate (NaPA) on adsorption of anionic surfactants n outcrop and resevoir sandstone corefloods. The dynamic retention studies were conducted with and without the presence of crude oil. Surfactant phase behavior studies were first conducted to identify surfactant blends that showed ultralow interfacial tension (IFT) with two crude oils at reservoir temperature (40°C). Base case dynamic retention data, in the absence of crude oil, was obtained for these surfactant formulations at their respective optimum salinities. NaPA was then added to these surfactant formulations and similar adsorption tests were conducted. Finally, oil recovery SP corefloods were conducted for each surfactant formulations, with and without adding NaPA, and oil recovery data including the surfactant retention was compared. Static adsorption of these surfactant formulations at their respective optimum salinities on crushed sandstone varied from 0.42-0.74 mg/g-rock. Their respective adsorptions lowered to 0.37-0.49 mg/g-rock on adding a small amount of NaPA. Surfactant retention in single-phase dynamic SP corefloods in the absence of crude oil in outcrop Berea cores was between 0.17 to 0.23 mg/g-rock. On adding a small amount of NaPA, the surfactant adsorption values lowered to 0.1 mg/g-rock. Oil recovery SP corefloods showed high oil recovery (~91% ROIP) and low surfactant retention (~0.1 mg/g-rock) on adding NaPA to the surfactant formulations.
Abstract This paper presents a large-scale experimental study of the compositional effect on produced bitumen properties in SAGD. The SAGD experiment used a sandpack in the cylindrical pressure vessel that was 1.22 m in length and 0.425 m in internal diameter. The pore volume of the sandpack was 58 L, and the porosity and permeability were 0.33 and 5.5 D, respectively. The sandpack was initially saturated with 93% bitumen and 7% deionized water. The SAGD experiment after preheating was operated mostly at a steam injection rate of 35 cm/min (cold-water equivalent) at 3600 kPa (244°C). The produced fluids (gas, oil, and water) were analyzed; e.g., ten oil samples were analyzed in terms of carbon number distribution (CND), the asphaltene content, density, and viscosity to investigate the compositional change of the produced bitumen. After the experiment, the sandpack was excavated and samples were taken for analysis of solid, water, oil, asphaltene, and sulfur contents. Experimental data (e.g., propagation of a steam chamber and production of oil and water) were history-matched by using a numerical reservoir simulator. Results showed that the produced bitumen was lighter and contained 1 to 5 wt% less asphaltenes than the original bitumen. Also, the remaining oil inside the steam chamber contained 6 wt% more asphaltenes. As a result, the produced bitumen was 1 to 6 kg/m less dense than the original bitumen. In the actual operations, bitumen is diluted with condensate to reduce the oil viscosity for pipeline shipping. This reduction in bitumen density corresponds to a reduction of the diluent cost by 5-10%. The produced bitumen became less dense with increasing steam-chamber volume. The history-matched simulation indicated that the progressively decreasing density of the produced bitumen can be attributed to the vaporization of the relatively volatile components in the remaining oil, and condensation of those components near the chamber edge. The history-matching also indicated that varying flow regimes (counter-current and co-current flow of water and oil) affected the oil recovery during in the SAGD experiment.
Abstract Asphaltene precipitation and deposition occur in the reservoir, near-wellbore, inside the tubing, and production facilities during primary, secondary, or tertiary production. As more water-flooded oil fields produce under miscible gas flooding, this problem becomes more common around the world. If asphaltene deposition occurs in the reservoir or wellbore, it can severely affect the economics of the field in terms of production loss, intervention cost, and the requirement for chemical additives, if necessary. In some severe cases, intervention would be impossible and side-track well needs to be drilled. Hence, the best strategy for oil production in asphaltenic reservoirs is to control asphaltene precipitation and deposition through prevention and remediation jobs to minimize the number of well shut-ins, the downtime of the wells, and the associated cost. In this paper, we reviewed the common asphaltene prevention and remediation techniques along with their pros and cons. Since removing asphaltene deposits from the problematic wells is relatively expensive and sometimes requires substantial downtime of the well, we focused on one of the prevention techniques (i.e., continuous solvent injection through capillary injection string), which has become more popular, to control asphaltene precipitation in the wellbore. We obtained the physical properties of an aromatic solvent from literature and then characterized it as a component to be used with PC-SAFT EOS. Subsequently, we used the in-house wellbore model to evaluate the effectiveness of the continuous solvent injection with different injection rates on preventing asphaltene precipitation and deposition along the wellbore.
Agrawal, Shivam (The University of Texas at Austin (now with Sensia Global, Houston)) | York, Jason (The University of Texas at Austin) | Foster, John T. (The University of Texas at Austin) | Sharma, Mukul M. (The University of Texas at Austin)
Summary Hydraulic fracture (HF) modeling is a multiscale and multiphysics problem. It should capture various effects, including those of in-situ stresses, poroelasticity, and reservoir heterogeneities at different length scales. A peridynamics (PD)-based hydraulic fracturing simulator has been demonstrated to reproduce this physics accurately. However, accounting for such details leads to a reduction in computational speed. In this paper, we present a novel coupling of the PD-based simulator with numerically efficient finite element methods (FEMs) and finite volume methods (FVMs) to achieve a significant improvement in computational performance. Unlike classical methods, such as FEM and FVM that solve differential equations, PD uses an integral formulation to circumvent the undefined spatial derivatives at crack tips. We implemented four novel coupling schemes of our PD-based simulator with FEM and FVM: static PD region scheme, dynamic PD region scheme, adaptive mesh refinement scheme, and dynamic mesh coarsening scheme. PD equations are solved using a refined mesh close to the fracture, whereas FE/FV equations are solved using a progressively coarser mesh away from the fracture. As the fracture grows, a dynamic conversion of FE/FV cells to PD nodes and adaptive mesh refinement are incorporated. To improve the performance further, the dynamic mesh coarsening scheme additionally converts the fine PD nodes back to coarse FE/FV cells as the HF grows in length. The coupling schemes are verified against the Kristianovich-Geertsma-de Klerk (KGD) fracture propagation problem. No spurious behavior is observed near the transition between PD and FE/FV regions. In the first three coupling schemes, the computational runtime for single fracture propagation is reduced by up to 10, 20, and 50 times, respectively, compared to a pure PD model. Laboratory experiments on the interaction of an HF with a natural fracture (NF) are revisited. The model captures complex fracture behavior, such as turning in the case of low stress contrast and low angle of interaction, kinking for higher stress contrast or higher angle of interaction, and fracture crossing for near-orthogonal NFs. Moreover, several previously reported phenomena, including fracture propagation at an angle to the principal stress directions, competing fracture growth from multiple closely spaced clusters, and interaction with layers of varying mechanical properties are successfully modeled. Thus, the coupling of PD with FEM and FVM offers an innovative and fundamentally comprehensive solution to alleviate the high computational costs typically associated with the pure PD-based hydraulic fracturing simulations. At the same time, these coupling schemes retain the versatility of the nonlocal PD formulation at modeling the evolution of arbitrary material damage, commonly observed during HF propagation in complex heterogeneous reservoirs.
Abstract During drilling operations, it is common to see pump pressure spikes when flow is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics-based modeling can be used to accurately predict the magnitude of pressure spikes. To model standpipe pressure behavior, machine learning techniques were combined with physics-based models via a rule-based, stochastic decision-making algorithm. To start, neural networks and deep learning models were trained using time-series drilling data. From there, physics-based equations that model the pressure required to break the mud's gel strength as well as the flow of non-Newtonian fluids through the entire circulation system were used to simulate standpipe pressure. Then, these two highly different methods for predicting/modeling standpipe pressure were combined by a hidden Markov model using a set of rules and transition probabilities. By combining machine learning and physics-based approaches, the best features of each model are leveraged by the hidden Markov model, yielding a more accurate and robust prediction of pressure. A similar result is not achievable with a purely data-driven black-box model, because it lacks a connection to the underlying physics. Our study highlights how drilling data analysis can be optimally leveraged. The overarching conclusion: hybrid modeling can more accurately predict pump pressure spikes and capture the transient events at flow initiation when compared to physics-based or machine learning models used in isolation. Moreover, the approach is not limited to pressure behavior but can be applied to a wide range of well construction operations. The proposed approach is easy to implement and the details of implementation are presented in this study. Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Pressure can be more accurately predicted through our proposed hybrid modeling, leading to safer, more optimized operations.
Ashok, Pradeepkumar (The University of Texas at Austin) | Chu, Jian (The University of Texas at Austin) | Witt-Doerring, Ysabel (The University of Texas at Austin) | Yan, Zeyu (The University of Texas at Austin) | Chen, Dongmei (The University of Texas at Austin) | van Oort, Eric (The University of Texas at Austin)
Abstract Identifying the root cause of damage of a pulled bit as soon as possible will aid preparation for future bit runs. Today, such bit damage analyses are often anecdotal, subjective and error-prone. The objective of this project was to develop a software algorithm to automatically analyze 2D bit images taken at the rig site, and to quickly identify the root cause of bit damage and failure. A labelled dataset was first created whereby the damage seen in bit photos was associated with the appropriate root cause of failure. Particular attention was given to the radial position of the cutters that were damaged. Using the 2D bit images (which can be obtained at the rig site), a convolutional neural network along with other image processing techniques were used to identify the individual cutters, their position on the bit, the degree of wear on each cutter. A classifier was then built to directly identify root cause of failure from these images. This work utilized a large dataset of wells which included multiple bit images, surface sensor data, downhole vibration data, and offset well rock strength information. This dataset helped relate the type of dysfunction as seen in the downhole and surface sensor data to the damage seen on the bit. This dataset however only covered some types of dysfunctions and some types of bit damage. It was therefore augmented with bit images for which the type of failure was determined through analysis by a subject- matter expert. A classifier was subsequently developed which properly identified the root causes of failure when the bit photo quality met certain minimum standards. One key observation was that bit images are not always captured appropriately, and this reduces the accuracy of the method. The automated forensics approach to Polycrystalline Diamond Compact (PDC) bit damage root cause analysis described in this paper can be performed using 2D bit photos that can be easily captured on a phone or camera at the rig site. By identifying the potential root causes of PDC damage through image processing, drilling parameters and bit selection can be optimized to prolong future bit life. The algorithm also enables uniformity in bit analysis across a company's operations, as well as the standardization of the process.
van Oort, Eric (The University of Texas at Austin) | Chen, Dongmei (The University of Texas at Austin) | Ashok, Pradeepkumar (The University of Texas at Austin) | Fallah, Amirhossein (The University of Texas at Austin)
Abstract Deep closed-loop geothermal systems (DCLGS) are introduced as an alternative to traditional enhanced geothermal systems (EGS) for green energy production that is globally scalable and dispatchable. Recent modeling work shows that DCLGS can generate an amount of power that is similar to EGS, while overcoming many of the downsides of EGS (such as induced seismicity, emissions to air, mineral scaling etc.). DCLGS wells can be constructed by leveraging and extending oil & gas extended reach drilling (ERD) and high-pressure high-temperature (HPHT) drilling expertise in particular. The objectives of this paper are two-fold. First, we demonstrate that DCLGS wells can generate power/electricity on a scale that is comparable to EGS, i.e. on the order of 40-55 MW per well. To this extent, we have developed a coupled hydraulic-thermal model, validated using oil and gas well cases, that can simulate various DCLGS well configurations. Secondly, we highlight the technology gaps and needs that still exist for economically drilling DCLGS wells, showing that it is possible to extend oil & gas technology, expertise and experience in ERD and HPHT drilling to construct complex DCLGS wells. Our coupled hydraulic-thermal sensitivity analyses show that there are key well drilling and design parameters that will ultimately affect DCLGS operating efficiency, including strategic deployment of managed pressure drilling / operation (MPD/MPO) technology, the use of vacuum-insulated tubing (VIT), and the selection of the completion in the high-temperature rock zones. Results show that optimum design and execution can boost geothermal power generation to 50 MW and beyond. In addition, historical ERD and HPHT well experience is reviewed to establish the current state-of-the-art in complex well construction and highlight what specific technology developments require attention and investment to make DCLGS a reality in the near-future (with a time horizon of ~10 years). A main conclusion is that DCLGS is a realistic and viable alternative to EGS, with effective mitigation of many of the (potentially show-stopping) downsides of EGS. Oil and gas companies are currently highly interested in green, sustainable energy to meet their environmental goals. DCLGS well construction allows them to actively develop a sustainable energy field in which they already have extensive domain expertise. DCLGS offers oil and gas companies a new direction for profitable business development while meeting environmental goals, and at the same time enables workforce retention, retraining and re-deployment using the highly transferable skills of oil and gas workers.