Zhu, Ziming (Colorado School of Mines) | Fang, Chao (Virginia Polytechnic Institute and State University) | Qiao, Rui (Virginia Polytechnic Institute and State University) | Yin, Xiaolong (Colorado School of Mines) | Ozkan, Erdal (Colorado School of Mines)
In nanoporous rocks, potential size/mobility exclusion and fluid-rock interactions in nano-sized pores and pore throats can turn the rock into a semi-permeable membrane, blocking or hindering the passage of certain molecules while allowing other molecules to pass freely. In this work, we conducted several experiments to investigate whether CO2 can mitigate the sieving effect on the hydrocarbon molecules flowing through Niobrara samples. Molecular dynamics simulations of adsorption equilibrium with and without CO2 were performed to help understand the trends observed in the experiments. The procedure of the experiments includes pumping of liquid binary hydrocarbon mixtures (C10 C17) of known compositions into Niobrara samples, collecting of the effluents from the samples, and analysis of the compositions of the effluents. A specialized experimental setup that uses an in-line filter as a mini-core holder was built for this investigation. Niobrara samples were cored and machined into 0.5-inch diameter and 0.7-inch length mini-cores. Hydrocarbon mixtures were injected into the mini-cores and effluents were collected periodically and analyzed using gas chromatography (GC). After observing the membrane behavior of the mini-cores, CO2 huff-n-puff was performed at 600 psi, a pressure much lower than the miscibility pressure. CO2 was injected from the production side to soak the sample for a period, then the flow of the mixture was resumed and effluents were analyzed using GC. Experimental results show that CO2 huff-n-puff in several experiments noticeably mitigated the sieving of heavier component (C17). The observed increase in the fraction of C17 in the produced fluid can be either temporary or lasting. In most experiments, temporary increases in flow rates were also observed. Molecular dynamics simulation results suggest that, for a calcite surface in equilibrium with a binary mixture of C10 and C17, more C17 molecules adsorb on the carbonate surface than the C10 molecules. Once CO2 molecules are added to the system, CO2 displaces C10 and C17 from calcite. The experimentally observed increase in the fraction of C17 thus can be attributed to the release of adsorbed C17. This study suggests that surface effects play a significant role in affecting flows and compositions of fluids in tight formations. In unconventional oil reservoirs, observed enhanced recovery from CO2 huff-n-puff could be partly attributed to surface effects in addition to the recognized gas-liquid interaction mechanisms.
Use of diverters for altering fluid distribution among created hydraulic fractures in horizontal wells has gained popularity in recent years, both for initial and re-fracturing treatments. Aims in initial fracturing treatments have included creating more uniform distribution of slurry within the created fractures, increasing stage efficiency by reducing the number of pumping stages while increasing the number of clusters per stage, increasing the number of fractures created in openhole completions, reducing interactions between fractures in adjacent horizontal wells, etc. In re-fracturing treatments, a popular application is for altering fluid distribution in wells re-treated without isolation between stages (Pump & Pray/Bullheading) with the intent of increasing the number of re-activated fractures and initiating new fractures through added perforations.
Engineering analysis of the mechanics of fluid diversion has not received the same degree of attention as its use. The reported discussions are often limited in their scope, two-dimensional in structure, and somewhat speculative in their conclusions.
This paper divides the targets of diversion into three categories; at the wellbore/perfs, near wellbore, and deeper inside the fracture. It divides the types of diverters into three categories, mechanical, solid particulate (including proppants), and chemical. The applications are divided into two categories, initial and re-fracturing, together with highlighting their differences and requirements for successful diversion. The paper discusses how presence of proppant changes the fluid distribution in favor of more conductive perforations. It considers the fracture as a three-dimensional structure, extending on both sides of the wellbore. It describes how different diverting agents cause fluid redistribution between the fractures, and the important role of proppant in some applications. It shows that as the target of fluid diversion moves away from the wellbore the chances of its success become smaller and more unpredictable, while also the time before effective diversion takes place becomes longer.
Comprehensive understanding of the mechanics of fluid diversion helps in the selection of the type of diverter and how best to deploy it for achieving specific objectives and results.
The paper provides a technical overview of an operator's Real-Time Drilling (RTD) ecosystem currently developed and deployed to all US Onshore and Deepwater Gulf of Mexico rigs. It also shares best practices with the industry through the journey of building the RTD solution: first designing and building the initial analytics system, then addressing significant challenges the system faces (these challenges should be common in drilling industry, especially for operators), next enhancing the system from lessons learned, and lastly, finalizing a fully integrated and functional ecosystem to provide a one-stop solution to end users.
The RTD ecosystem consists of four subsystems as shown in architecture
RTD ecosystem architecture
RTD ecosystem architecture
All of these subsystems are fully integrated and interact with each other to function as one system, providing a one-stop solution for real-time drilling optimization and monitoring. This RTD ecosystem has become a powerful decision support tool for the drilling operations team. While it was a significant effort, the long term operational and engineering benefits to operators designing such a real-time drilling analytics ecosystem far outweighs the cost and provides a solid foundation to continue pushing the historical limitations of drilling workflow and operational efficiency during this period of rapid digital transformation in the industry.
Unal, Ebru (University of Houston) | Rezaei, Ali (University of Houston) | Siddiqui, Fahd (University of Houston) | Likrama, Fatmir (Halliburton) | Soliman, M. (University of Houston) | Dindoruk, Birol (Shell International Exploration and Production, Inc.)
In the last decade, technical advancements have greatly improved the design and execution efficiency of well completions, leading to improved recovery from unconventional reservoirs. However, analyzing fracture diagnostic tests in unconventional plays are still challenging due to high uncertainty in predictive capabilities in the context of fracture dynamics during treatment. The main objective of this study is to identify fracture behavior during injection and pressure fall-off periods in hydraulic fracturing treatments and diagnostic fracture injection tests (DFIT), respectively.
In this study, discrete wavelet transformation (DWT) was used to analyze real field injection and fall-off data in the wavelet domain. The analyzed data are from multi-stage hydraulic fracturing operations and DFIT in unconventional horizontal wells. DWT coefficients reveal very crucial information related to the nature of the events within recorded signals; they also reveal various patterns that are hard to recognize otherwise. The high-frequency components of the pressure and rate signals (detail coefficients) that are calculated by the wavelet transformation determine localization and separation of various events. We compared the identified events for injection and fall-off periods with moving reference point (MRP) and G-function analysis, respectively.
The main advantage of our proposed approach is that it is based on real-time data and does not require any assumptions related to existing or created fractures. Also, it is very sensitive to physical changes in the system; thus, it reveals hidden information related to those changes. Consequently, the energy of detail coefficients represents several events at different frequencies. We used pseudo-frequency of wavelet coefficients as a diagnostic tool for an accurate comparison of fracture propagation and fracture closure events to determine similarities and differences between them. For example, the signal energy of detail coefficients from the wavelet transformation of hydraulic fracturing data demonstrates abrupt frequency changes during dilation or fracture height growth during fracture propagation. Therefore, we were able to identify those events by energy density analysis in both time and pseudo-frequency domains in an objective manner, which otherwise was not possible with conventional methodologies such as G- function derivative analysis.
This paper details the successful methodology for effective implementation of a new fracture diagnostic technique for fracturing operations or DFITs in unconventional horizontal wells. This new fracture diagnostic method does not require any reservoir or fracture pre-assumptions; it mainly relies on the pressure behavior, which is a result of various events at different frequencies. Pressure fall-off behavior of a DFIT gives essential information related to closure event of the created mini-fracture. Identification of these events at different pseudo-frequency ranges improves the understanding of the dynamic fracture behavior also the characteristics of the reservoir. Unlike many other diagnostic techniques, this data-driven approach requires minimum input/data for analysis. This approach also lends itself to real-time application quite easily.
Cui, Xiaona (Texas A&M University and Northeast Petroleum University) | Song, Kaoping (China University of Petroleum - Beijing) | Yang, Erlong (Northeast Petroleum University) | Jin, Tianying (Texas A&M University) | Huang, Jingwei (Texas A&M University) | Killough, John (Texas A&M University) | Dong, Chi (Northeast Petroleum University)
The phase behavior shifts of hydrocarbons confined in nanopores have been extensively verified with experiments and molecular dynamics simulations. However, the impact of confinement on large-scale reservoir production is not fully understood. This work is to put forward a valid method to upscale the pore-scale fluid thermodynamic properties to the reservoir-scale and then incorporate it into our in-house compositional simulator to examine the effect of confinement on shale reservoir production.
Firstly, a pore-scale fluid phase behavior model is developed in terms of the pore type and pore size distribution (PSD) in the organic-rich shale reservoir using our modified Peng-Robinson equation of state (PR-C EOS) which is dependent on the size-ratio of fluid molecule dynamic diameter and the pore diameter. And the fluid composition distribution and PVT relation of fluids in each pore can be determined as the thermodynamic equilibria are achieved in the whole system. Results show that the initial fluid composition distribution is not uniform for different pore types and pore sizes. Due to the effect of confinement, heavier components are retained in the macropore, and lighter components are more liable to accumulate in the confined nanopores. Then an upscaled equation of state is put forward to model the fluid phase behavior at the reservoir-scale based on our modified PR-C EOS using a pore volume-weighted average method. This upscaled EOS is validated with the pore-scale fluid phase behavior simulation results and can be used for compositional simulation. Finally, two different reservoir fluids from the Eagle Ford organic-rich shale reservoir are simulated using our in-house compositional simulator to investigate the effect of confinement on production. In addition to the critical property shift which can be described by our upscaled PR-C EOS, capillary pressure is also taken into account into the compositional simulation. Results show that the capillary pressure has different effects on production in terms of the fluid type, leading to a lower producing Gas/Oil ratio (GOR) for black oil and a higher GOR for gas condensate. Critical property shift has a consistent effect on both the black oil and gas condensate, resulting in a lower GOR. It should be noted that the effect of capillary pressure on production is suppressed for both fluids with the shifted critical property.
Potapenko, Dmitriy (Schlumberger) | Theuveny, Bertrand (Schlumberger) | Williams, Ryan (Schlumberger) | Moncada, Katharine (Schlumberger) | Campos, Mario (Schlumberger) | Spesivtsev, Pavel (Schlumberger) | Willberg, Dean (Schlumberger)
Highly efficient multi-stage hydraulic fractured horizontal wellbores are the dominant completion method for many basins worldwide. One potential weakness of multi-stage hydraulic fracturing is that the later stages of the completion workflow – frac-plug drill out (FPDO) and flowback – cause large pressure fluctuations and transient flows through the perforation clusters that coincide with a period of low closure stress in the fractures. The proppant packs in the fractures during this period are fragile and prone to failure. Previously reported results show that flowback and initial production practices have a major impact on proppant production, maintenance and disposal costs and the subsequent well performance. In this paper the results from over 200 FPDO and flowback operations from the United States and Argentina are reviewed. These results show that maintaining a balanced flowrate during FPDO operations is critical for minimizing inadvertent damage to the hydraulic fracture network.
The FPDO flowrate balance is the difference between the coiled tubing injection and annular return flowrates. The magnitude and sign of the balance corresponds to the instantaneous flowrate through the open perforation clusters into or out of the hydraulic fracture network. A positive balance rate, or overbalance, injects fluid into the fracture system. A negative balance rate, or underbalance, produces stimulation or formation fluids from the fracture network. Sudden changes between these two regimes creates local flows that can be severe enough to flush large quantities of proppant out of the fractures. Our results show that high-frequency multiphase flowmeters simplify the process of maintaining balance (no inflow, no outflow). Furthermore, close monitoring of any imbalance that develops, and rapid control of the surface choke and injection rate, can provide for an efficient operation while protecting the integrity of the fracture system.
Early monitoring of flowback and production with a high frequency flowmeter was shown to be extremely useful technique for optimizing well productivity during well clean-up. This paper also shows how a dual energy gamma ray multiphase flowmeter successfully quantified proppant produced during FPDO and flowback. Examples of the dynamics of sand production are shown, as well as correlations to events of excessive underbalance conditions.
At the end of the paper we show that most of the highlighted problems can be solved through making changes to the well construction workflow and accounting for relationships between various well operations. Incorporation of this workflow enables early prediction of well performance issues and their efficient resolution.
Hydrocarbon production from shale formation has become an essential part of the global energy supply in the past decade. The life of a project in an unconventional play significantly depends on the prediction of Estimated Ultimate Recovery (EUR). However, the conventional methodology to predict EUR becomes less accurate for shale formations, which significantly affects the economics returns of projects in unconventional plays. The objective of this article is to investigate the most important independent variables, including petrophysics and completion parameters, to estimate EUR by the machine learning algorithm. A novel machine learning model based on Random Forest Regression is introduced to predict EUR and to rank the importance of the independent variables.
In this article, production/petrophysics/engineering/ data with more than 25 variables from 4000 wells in Eagle Ford is summarized for analysis. The data is collected from production monitoring, well logging, well testing, seismic interpretation and lab experiments. This paper has three major components. Firstly, a multivariate linear regression model is created to predict the overall EUR. Secondly, the spatial autocorrelation analysis is carried out to identify whether spatial variables could affect the accuracy of the multivariate regression model. Thirdly, the Random Forest Regression models are trained to examine their reliability in predicting EUR with spatially autocorrelated data. The importance of key predictors is also identified. The final models are tuned with optimized hyperparameters. Through the article, the predictive capabilities of each Random Forest Regression model are discussed in detail to understand the physics behind unconventional hydrocarbon production mechanisms.
The results and workflow presented in this paper are insightful and novel. Firstly, we test the multivariate regression analysis with all the petrophysics and completion variables using the backward elimination method. This widely used model has a limitation of excluding the spatial information. In order to identify the impact of spatial variable, we calculate the Moran's Index and find out that the data in this study is clustered or spatially autocorrelated. The p-value for EUR, Oil EUR and Gas EUR are 0.000002, 0.000000 and 0.12, which all reject the null hypothesis that the data is randomly distributed. To include the spatial information in the prediction, we use advanced machine learning technology, Random Forest, to predict the EUR with a combination of petrophysics, completion variables and spatial information. The key variables to predict EUR, Oil EUR and Gas EUR by the Random Forest Regression are identified. However, the importance of the key variables to predict Oil EUR and Gas EUR are different. Therefore, we split the overall EUR Random Forest Regression model (57% explained) into two prediction models, one for Oil EUR prediction and one for Gas EUR prediction. The Gas EUR Random Forest Regression model has better performance (76% explained) compared to the Oil EUR Random Forest Regression model (60% explained).
This study provides a deeper understanding of unconventional hydrocarbon production prediction from a big data perspective, and proposes a novel and reliable machine-learning model to predict EUR to evaluate economic returns in Eagle Ford. Compared to the traditional multivariate regression model, our Random Forest Regression models are more reliable. In addition, the Random Forest technique is able to rank the importance of the relevant independent variables, and the rank of importance can be applied to guide and to improve data collection and model training for further study on this topic. The workflow presented in this article can be also used to train data for other unconventional resource plays.
The Denver Section Emerging Leaders Program (ELP) has been up and running for 1 year now. Thus far, we have held several networking socials and luncheon meetings with lecturers from around the Rocky Mountain region. Topics have included professional licensing for petroleum engineers and challenges facing the natural gas market in the Rocky Mountain region. Industry financial and other resource support has been overwhelming. Thanks to all who have contributed.
The Powder River Basin has emerged over the past year as the latest source of oil production growth for the Lower 48. Companies ranging from a reborn Samson Resources to US onshore mainstays Devon, Chesapeake, and EOG are now betting on the basin to become a long-term core asset. Colorado’s industry lacks the size, variety, and Wild West characteristics of Texas, but that is precisely why the Centennial State’s oil production is surging to record levels. This paper describes a comprehensive field study of eight horizontal wells deployed in the stacked Niobrara and Codell reservoirs in the Wattenberg Field (Denver-Julesburg Basin).