Hydraulic fracturing is the most effective stimulation process to maximize resource extraction in unconventional reservoirs. However, water leakoff into the matrix of unconventional reservoirs, whether from a frac stage or from a pad placed in a parent well and pressured up to prevent frac hits, results in relative permeability reduction, decrease in hydrocarbon production rate and possible formation damage. This paper reports the application of a foam designed with an innovative combination of nanoparticles and surfactants to create a highly stable fluid with a low leak-off rate and non-damaging characteristics. A series of laboratory tests were conducted on tight core samples with variable permeability ranging from micro-to millidarcies, using different fluid systems including gas, water, and foam. A uniquely designed coreflood setup was used to imitate the wellbore/fracture-matrix condition. A fracture/matrix pressure difference of 1500 psi was used to evaluate the performance of each fluid with respect to maintaining pressure over time and minimizing leak off at a temperature of 80 °C. The test results show that the laboratory-designed nanofoam can effectively maintain elevated pressure in the fracture sufficient to reduce frac hits. The pressure depleted to 50% of original pressure in less than 3 hours when using gas or water and less than 15 hours in case of surfactant foam. However, the nanofoam maintained a pressure higher than 50% of the original pressure for more than 70 hours. The leak-off volume of the foam was low, and the foam could be easily cleaned up with no formation damage (i.e., no change in core permeability). This study reveals the potential of a highly stable foam as a fast and reliable method to prevent frac hit problems, saving operational cost and reducing water usage without compromising the well productivity. This foam can be potentially used as a base fracture fluid due to its high viscosity, high stability, and non-damaging characteristics.
{"title":"Effective Pressure Maintenance and Fluid Leak-Off Management Using Nanoparticle-Based Foam","authors":"A. Telmadarreie, Shuliang Li, S. Bryant","doi":"10.2118/208949-ms","DOIUrl":"https://doi.org/10.2118/208949-ms","url":null,"abstract":"\u0000 Hydraulic fracturing is the most effective stimulation process to maximize resource extraction in unconventional reservoirs. However, water leakoff into the matrix of unconventional reservoirs, whether from a frac stage or from a pad placed in a parent well and pressured up to prevent frac hits, results in relative permeability reduction, decrease in hydrocarbon production rate and possible formation damage.\u0000 This paper reports the application of a foam designed with an innovative combination of nanoparticles and surfactants to create a highly stable fluid with a low leak-off rate and non-damaging characteristics. A series of laboratory tests were conducted on tight core samples with variable permeability ranging from micro-to millidarcies, using different fluid systems including gas, water, and foam. A uniquely designed coreflood setup was used to imitate the wellbore/fracture-matrix condition. A fracture/matrix pressure difference of 1500 psi was used to evaluate the performance of each fluid with respect to maintaining pressure over time and minimizing leak off at a temperature of 80 °C.\u0000 The test results show that the laboratory-designed nanofoam can effectively maintain elevated pressure in the fracture sufficient to reduce frac hits. The pressure depleted to 50% of original pressure in less than 3 hours when using gas or water and less than 15 hours in case of surfactant foam. However, the nanofoam maintained a pressure higher than 50% of the original pressure for more than 70 hours. The leak-off volume of the foam was low, and the foam could be easily cleaned up with no formation damage (i.e., no change in core permeability).\u0000 This study reveals the potential of a highly stable foam as a fast and reliable method to prevent frac hit problems, saving operational cost and reducing water usage without compromising the well productivity. This foam can be potentially used as a base fracture fluid due to its high viscosity, high stability, and non-damaging characteristics.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84225618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A data-driven workflow was developed to monitor electrical submersible pump (ESP) health using an anomaly detection method with high-frequency sensor data. The workflow would help maximize the run life of ESPs while reducing the cost of maintenance. The new workflow contrasts with conventional field maintenance which is often reactive and incurs additional downtime in logistics and inventory management in diagnosing the issues and taking the recommended actions. In contrast, using machine learning (ML) concepts can save operating costs, especially in the case of the ESPs widely used for artificial lift. Many operators augment ESPs with high-frequency (HF) sensors to monitor their performance, but much of this information remains either unused or partially used in post-failure analysis. The application of ML concepts in understanding ESP operational behavior complements the existing domain practice. The workflow we describe in this paper begins with domain knowledge and exploratory statistical analysis to find the key performance indicators (KPIs) related to ESP failure. Feature engineering and advanced ML techniques are used to build and test healthy ESP models for each selected KPI. Multiple health signals are fused to improve the performance of anomaly detection using historical ESP failure data and pullout reports as benchmarks. In a test of the workflow, the model was trained on the data from a group of active producing wells with reported historical events, failures, and pullout reports. The data contained several well events and several reported failures. This information was used to fine-tune the alarm thresholds for the health indicators. The model was able to detect approximately 70% of failure events (true positive rate) in the data set. The false alarm rates for the configured model were approximately at 20% (false positive rate). The solution can be implemented in a dashboard to monitor ESP KPIs and show health alarms. These alarms can be further prioritized based on the failure probability and remaining useful life of the ESP. The health signal degradation patterns can be captured and learned to predict the remaining useful life of the ESPs, thus enabling operators to allocate and prioritize maintenance resources. In addition, the analysis of ESP pullout reports can provide insight into the relationship between health signals and root causes of the failure, which can be structured into a formal Bayesian network to provide automatic root cause interpretation The data-driven approach takes advantage of the vast amount of reservoir, production, and facilities data and provides insights into nonlinear multidimensional relationships between parameters to better understand and optimize field development and to adopt a proactive approach toward equipment maintenance.
{"title":"Integrating Domain Knowledge with Machine Learning to Optimize Electrical Submersible Pump Performance","authors":"Abhishek Sharma, P. Songchitruksa, R. Sinha","doi":"10.2118/208972-ms","DOIUrl":"https://doi.org/10.2118/208972-ms","url":null,"abstract":"\u0000 A data-driven workflow was developed to monitor electrical submersible pump (ESP) health using an anomaly detection method with high-frequency sensor data. The workflow would help maximize the run life of ESPs while reducing the cost of maintenance. The new workflow contrasts with conventional field maintenance which is often reactive and incurs additional downtime in logistics and inventory management in diagnosing the issues and taking the recommended actions. In contrast, using machine learning (ML) concepts can save operating costs, especially in the case of the ESPs widely used for artificial lift.\u0000 Many operators augment ESPs with high-frequency (HF) sensors to monitor their performance, but much of this information remains either unused or partially used in post-failure analysis. The application of ML concepts in understanding ESP operational behavior complements the existing domain practice. The workflow we describe in this paper begins with domain knowledge and exploratory statistical analysis to find the key performance indicators (KPIs) related to ESP failure. Feature engineering and advanced ML techniques are used to build and test healthy ESP models for each selected KPI. Multiple health signals are fused to improve the performance of anomaly detection using historical ESP failure data and pullout reports as benchmarks.\u0000 In a test of the workflow, the model was trained on the data from a group of active producing wells with reported historical events, failures, and pullout reports. The data contained several well events and several reported failures. This information was used to fine-tune the alarm thresholds for the health indicators. The model was able to detect approximately 70% of failure events (true positive rate) in the data set. The false alarm rates for the configured model were approximately at 20% (false positive rate). The solution can be implemented in a dashboard to monitor ESP KPIs and show health alarms. These alarms can be further prioritized based on the failure probability and remaining useful life of the ESP. The health signal degradation patterns can be captured and learned to predict the remaining useful life of the ESPs, thus enabling operators to allocate and prioritize maintenance resources. In addition, the analysis of ESP pullout reports can provide insight into the relationship between health signals and root causes of the failure, which can be structured into a formal Bayesian network to provide automatic root cause interpretation\u0000 The data-driven approach takes advantage of the vast amount of reservoir, production, and facilities data and provides insights into nonlinear multidimensional relationships between parameters to better understand and optimize field development and to adopt a proactive approach toward equipment maintenance.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82068449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type-curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive and the non-uniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler's model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many SAGD well-pairs tends to deviate from a set of pre-defined type curves. Historical well data is a digital asset that can be utilized to develop machine learning or data-driven models for the purpose of production forecasting. These models involve lower computational effort compared to numerical simulators and offer better accuracy compared to proxy models based on Butler's equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and "What If" scenario analysis. This paper presents a novel machine learning workflow that includes a predictive model development using the random forest algorithm, clustering, Bayesian updating, Monte Carlo sampling, and genetic algorithm for accurate forecasting of real-world SAGD injection and production data, and optimization. The training dataset involves field data that is typically available for a SAGD well-pair (e.g. operational data, geological, and well design parameters). Just as importantly, this machine learning workflow can update predictions in real-time, be applied for the quantification of the uncertainties associated with the forecasts, and optimize steam allocation, making it a practical tool for development planning and field-wide optimization. To the best of the author's knowledge, this is the first time that machine learning algorithms have been applied to a SAGD data set of this size.
{"title":"Manuscript Title","authors":"Seyide Hunyinbo, P. Azom, A. Ben-Zvi, J. Leung","doi":"10.2118/208962-ms","DOIUrl":"https://doi.org/10.2118/208962-ms","url":null,"abstract":"\u0000 Field development planning and economic analysis require reliable forecasting of bitumen production. Forecasting at the field level may be done using reservoir simulations, type-curve analysis, and other (semi-)analytical techniques. Performing reservoir simulation is usually computationally expensive and the non-uniqueness of a history-matched solution leads to uncertainty in the model predictions and production forecasts. Analytical proxies, such as Butler's model and its various improvements, allow for sensitivity studies on input parameters and forecasting under multiple operational scenarios and geostatistical realizations to be conducted rather quickly, despite being less accurate than reservoir simulation. Similar to their reservoir simulation counterparts, proxy models can also be tuned or updated as more data are obtained. Type curves also facilitate efficient reservoir performance prediction; however, in practice, the performance of many SAGD well-pairs tends to deviate from a set of pre-defined type curves.\u0000 Historical well data is a digital asset that can be utilized to develop machine learning or data-driven models for the purpose of production forecasting. These models involve lower computational effort compared to numerical simulators and offer better accuracy compared to proxy models based on Butler's equation. Furthermore, these data-driven models can be used for automated optimization, quantification of geological uncertainties, and \"What If\" scenario analysis.\u0000 This paper presents a novel machine learning workflow that includes a predictive model development using the random forest algorithm, clustering, Bayesian updating, Monte Carlo sampling, and genetic algorithm for accurate forecasting of real-world SAGD injection and production data, and optimization. The training dataset involves field data that is typically available for a SAGD well-pair (e.g. operational data, geological, and well design parameters). Just as importantly, this machine learning workflow can update predictions in real-time, be applied for the quantification of the uncertainties associated with the forecasts, and optimize steam allocation, making it a practical tool for development planning and field-wide optimization. To the best of the author's knowledge, this is the first time that machine learning algorithms have been applied to a SAGD data set of this size.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77704219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rate-transient analysis (RTA) is routinely used for unconventional production data analysis and forecasting. RTA uses rate normalization along with material balance time to estimate:(a) flow regimes, (b) the time of end of transient flow, and (c) the drainage volume and to thereby predict the estimated ultimate recovery (EUR) of wells. However, rate normalization approximates deconvolution, and material balance time is only strictly applicable for constant-pressure or constant-rate systems. This work investigates the validity of rate normalization and material balance time for synthetic and tight-oil well examples producing under variable bottomhole flowing pressure (BHP) conditions. This work generates synthetic examples with different BHP histories and uses deconvolution to estimate a constant-pressure rate/material balance time response. First, we plot and compare the normalized rate vs. material balance time, the deconvolved rate/material balance time and the unit-pressure-drop rate vs. constant-pressure material balance time. The latter plot represents the constant-pressure solution of the 1D flow of a slightly compressible fluid (reference case). Second, we evaluate the plots of material balance time vs. time, deconvolved material balance time vs. time and constant-pressure material balance time vs. time. Third, we fit the reference type curve to a plot of the normalized rate vs. material balance time and the deconvolved rate vs. deconvolved material balance time to determine the reservoir properties to then estimate the EUR using time superposition. We conclude by illustrating the application of these steps to tight-oil wells in which we use deconvolution to estimate the unit-pressure-drop rate and the constant-pressure material balance time. The results of this study are the following. First, BHP changes alter the slope of the log-log normalized rate-vs. material balance time plot. Second, BHP variations introduce error to the behavior of the material balance time vs. time function leading to incorrect estimates of the time of end of transient flow. Consequently, normalized-pressure rate and material balance time are not always reliable variables to properly identify the flow regime(s) and thus, to correctly estimate the time of end of transient flow and EUR. Alternatively, applying deconvolution to rigorously account for the pressure variations and generate the unit-pressure-drop rate and the constant-pressure material balance time solves these problems. This paper investigates the validity of rate normalization and material balance time in RTA of unconventional reservoirs. Caution is needed when applying rate normalization and material balance time since these might lead to incorrect estimates of the time of end of transient flow and EUR. For this reason, deconvolution should be included as an integral part of the RTA workflow.
{"title":"Deconvolution Overcomes the Limitations of Rate Normalization and Material Balance Time in Rate-Transient Analysis of Unconventional Reservoirs","authors":"L. R. Ruiz Maraggi, L. Lake, M. P. Walsh","doi":"10.2118/208948-ms","DOIUrl":"https://doi.org/10.2118/208948-ms","url":null,"abstract":"\u0000 Rate-transient analysis (RTA) is routinely used for unconventional production data analysis and forecasting. RTA uses rate normalization along with material balance time to estimate:(a) flow regimes, (b) the time of end of transient flow, and (c) the drainage volume and to thereby predict the estimated ultimate recovery (EUR) of wells. However, rate normalization approximates deconvolution, and material balance time is only strictly applicable for constant-pressure or constant-rate systems. This work investigates the validity of rate normalization and material balance time for synthetic and tight-oil well examples producing under variable bottomhole flowing pressure (BHP) conditions.\u0000 This work generates synthetic examples with different BHP histories and uses deconvolution to estimate a constant-pressure rate/material balance time response. First, we plot and compare the normalized rate vs. material balance time, the deconvolved rate/material balance time and the unit-pressure-drop rate vs. constant-pressure material balance time. The latter plot represents the constant-pressure solution of the 1D flow of a slightly compressible fluid (reference case). Second, we evaluate the plots of material balance time vs. time, deconvolved material balance time vs. time and constant-pressure material balance time vs. time. Third, we fit the reference type curve to a plot of the normalized rate vs. material balance time and the deconvolved rate vs. deconvolved material balance time to determine the reservoir properties to then estimate the EUR using time superposition. We conclude by illustrating the application of these steps to tight-oil wells in which we use deconvolution to estimate the unit-pressure-drop rate and the constant-pressure material balance time.\u0000 The results of this study are the following. First, BHP changes alter the slope of the log-log normalized rate-vs. material balance time plot. Second, BHP variations introduce error to the behavior of the material balance time vs. time function leading to incorrect estimates of the time of end of transient flow. Consequently, normalized-pressure rate and material balance time are not always reliable variables to properly identify the flow regime(s) and thus, to correctly estimate the time of end of transient flow and EUR. Alternatively, applying deconvolution to rigorously account for the pressure variations and generate the unit-pressure-drop rate and the constant-pressure material balance time solves these problems.\u0000 This paper investigates the validity of rate normalization and material balance time in RTA of unconventional reservoirs. Caution is needed when applying rate normalization and material balance time since these might lead to incorrect estimates of the time of end of transient flow and EUR. For this reason, deconvolution should be included as an integral part of the RTA workflow.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89515129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Irani, S. Ghannadi, Nick Daprocida Nick Daprocida, Pierre Lacoste-Bouchet, Vince DiStefano
There are large regions of the subsurface where the temperature is sufficiently hot to generate geothermal electricity within reasonable drilling depths, but the formation does not have enough permeability to create heated water productivity. The primary bottleneck is the difficulty of achieving adequate flow rate in such geothermal reservoirs. These formations are referred as hard dry rock (HDR). In these formations two common applications are conducted: Enhanced (or Engineered) geothermal systems (EGS) and Advanced geothermal systems (AGS). AGS is a closed-loop system that are built on wells drilled that connect with each other allowing a heat exchange-type to set up beneath the surface. There has recently been a lot of attention to the concept of ‘closed-loop geothermal’. The concept is still evolving and few startups such as E2E Energy Solutions, Eavor Technologies, and Green Fire Energy suggested different types of the closed-loop to optimize the technology. Since the technology only relies on conduction as the only source of heat transfer to improve the economics of the closed-loop E2E Energy Solutions suggested to use the hydraulic fracture to increase the surface area between the well and the geothermal reservoir. Such process is called Enhanced Geothermal Reservoir Recovery System (EGRRS). In the E2E's EGRRS process, a fluid would be produced from an existing hot subterranean aquifer reservoir, close to a favorable geothermal zone, instead of creating the whole loop from the surface. The fluid withdrawn from a hot subterranean reservoir would be contacted with a hydraulically fractured zone in the geothermal zone, which would result to additional energy transfer and therefore a higher enthalpy once the fluid reaches the surface. The plan is to reach temperatures above 200°C that greatly increasing the electrical generation potential. Although to implement the EGRRS process we are using the common oil and gas practice but to optimize and design the process using current modelling techniques is not achievable. Current simulation schemes cannot model such complex system, and to resolve this, a new framework must be designed to resolve the challenge. In this paper we present a new method that calculate rate to each branch by a new iterative approach that resolve the problem on how fractions of different pipe should be solved. Since the closed-loop wells are connected to the subterranean aquifer reservoirs and operator required to keep the WHP at constant pressure, there is another layer of iteration that required to solve for fraction on each pipe and BHP at the aquifer. Finally, to model the heating in the fractured zone, a new resistance is added to the model to mimic the heating exchange between the fracture and the fluid? and also the fracture and the radiator formation.
在合理的钻井深度内，地下有大片区域的温度足够高，可以产生地热发电，但地层没有足够的渗透率来产生热水产能。主要的瓶颈是这种地热储层难以达到足够的流量。这些岩层被称为硬干岩(HDR)。在这些地层中进行了两种常见的应用:增强型(或工程)地热系统(EGS)和高级地热系统(AGS)。AGS是一个建立在相互连接的井上的闭环系统，允许在地表下建立热交换类型。最近有很多人关注“闭环地热”的概念。这一概念仍在不断发展，一些初创公司，如E2E Energy Solutions、Eavor Technologies和Green Fire Energy，提出了不同类型的闭环来优化技术。由于该技术仅依赖传导作为传热的唯一来源来提高闭环E2E能源解决方案的经济性，因此建议使用水力裂缝来增加井与地热储层之间的表面积。这一过程被称为增强型地热储层开采系统(EGRRS)。在E2E的EGRRS过程中，流体将从靠近有利地热带的现有热地下含水层储层中产生，而不是从地表产生整个循环。从地下热储中取出的流体将与地热区的水力裂缝区接触，这将导致额外的能量传递，因此，一旦流体到达地表，焓就会更高。计划是达到200°C以上的温度，从而大大提高发电潜力。虽然为了实施EGRRS流程，我们使用了常见的油气实践，但使用当前的建模技术来优化和设计流程是不可实现的。当前的仿真方案无法模拟如此复杂的系统，为了解决这一问题，必须设计一个新的框架来解决这一挑战。本文提出了一种新的方法，用新的迭代法计算每个分支的速率，解决了不同管道的分数如何求解的问题。由于闭环井与地下含水层储层相连，作业者需要将水泵压保持在恒定压力下，因此还需要进行另一层迭代，以求解每根管道上的分数和含水层上的BHP。最后，为了模拟裂缝区域的加热，在模型中加入一个新的阻力来模拟裂缝与流体之间的热交换。还有裂缝和辐射体的形成。
{"title":"On Numerical Modelling of the Hydraulic-Fractured Closed Loop Systems: Single Producer","authors":"M. Irani, S. Ghannadi, Nick Daprocida Nick Daprocida, Pierre Lacoste-Bouchet, Vince DiStefano","doi":"10.2118/208945-ms","DOIUrl":"https://doi.org/10.2118/208945-ms","url":null,"abstract":"\u0000 There are large regions of the subsurface where the temperature is sufficiently hot to generate geothermal electricity within reasonable drilling depths, but the formation does not have enough permeability to create heated water productivity. The primary bottleneck is the difficulty of achieving adequate flow rate in such geothermal reservoirs. These formations are referred as hard dry rock (HDR). In these formations two common applications are conducted: Enhanced (or Engineered) geothermal systems (EGS) and Advanced geothermal systems (AGS). AGS is a closed-loop system that are built on wells drilled that connect with each other allowing a heat exchange-type to set up beneath the surface. There has recently been a lot of attention to the concept of ‘closed-loop geothermal’. The concept is still evolving and few startups such as E2E Energy Solutions, Eavor Technologies, and Green Fire Energy suggested different types of the closed-loop to optimize the technology. Since the technology only relies on conduction as the only source of heat transfer to improve the economics of the closed-loop E2E Energy Solutions suggested to use the hydraulic fracture to increase the surface area between the well and the geothermal reservoir. Such process is called Enhanced Geothermal Reservoir Recovery System (EGRRS).\u0000 In the E2E's EGRRS process, a fluid would be produced from an existing hot subterranean aquifer reservoir, close to a favorable geothermal zone, instead of creating the whole loop from the surface. The fluid withdrawn from a hot subterranean reservoir would be contacted with a hydraulically fractured zone in the geothermal zone, which would result to additional energy transfer and therefore a higher enthalpy once the fluid reaches the surface. The plan is to reach temperatures above 200°C that greatly increasing the electrical generation potential.\u0000 Although to implement the EGRRS process we are using the common oil and gas practice but to optimize and design the process using current modelling techniques is not achievable. Current simulation schemes cannot model such complex system, and to resolve this, a new framework must be designed to resolve the challenge. In this paper we present a new method that calculate rate to each branch by a new iterative approach that resolve the problem on how fractions of different pipe should be solved. Since the closed-loop wells are connected to the subterranean aquifer reservoirs and operator required to keep the WHP at constant pressure, there is another layer of iteration that required to solve for fraction on each pipe and BHP at the aquifer. Finally, to model the heating in the fractured zone, a new resistance is added to the model to mimic the heating exchange between the fracture and the fluid? and also the fracture and the radiator formation.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77370002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Asphaltene may destabilize during the oil recovery, transportation, and processing and cause significant flow assurance problems that negatively affect the operational expenditures (OPEX). Modeling investigation of asphaltene precipitation and consequently deposition is a vital research component in flow assurance requiring the accurate description of the phenomena under various operational conditions. The structure of asphaltene molecules and the presence of heteroatoms play a significant role in the intermolecular forces and the mechanism of asphaltene aggregation. Nevertheless, the intermolecular forces, e.g., polar forces, and their addition to thermodynamic modeling of asphaltene phase behavior still need investigation. While the traditional equation of state (EoS), e.g., cubic EoS, does not provide any special treatment to polar energy, the π-π interaction and polar effect can be mapped into the EoS using a separate polar term. In this research, we use cubic EoS, cubic plus polar (CPP) EoS, and molecular dynamics (MD) (three different modeling approaches) to analyze the effect of asphaltene structure and operational conditions on the precipitation phenomenon. Comparing the error associated with correlation and prediction results of the models, we show that the CPP approach using optimization to tune parameters of the EoS is the most reliable approach, followed by CPP EoS using MD to find dipole moment for the aryl-linked core asphaltene structure. The CPP EoS and MD optimizing island structure for asphaltene is the third-best model, and SRK EoS is a less efficient approach. Considering the values for dipole moment and molecular weight of asphaltene, along with correlation and prediction ability of the techniques, it is revealed that polar forces can be considered in a separate term in addition to van der Waals force to increase the model efficiency. Moreover, the aryl structure with a 750 g/mol molecular weight and one/two thiophene/pyridine group is the most proper asphaltene structure.
{"title":"A CPP Model to Asphaltene Precipitation; Mapping p-p Interactions onto an Equation of State","authors":"S. Alimohammadi, L. James, S. Zendehboudi","doi":"10.2118/208942-ms","DOIUrl":"https://doi.org/10.2118/208942-ms","url":null,"abstract":"\u0000 Asphaltene may destabilize during the oil recovery, transportation, and processing and cause significant flow assurance problems that negatively affect the operational expenditures (OPEX). Modeling investigation of asphaltene precipitation and consequently deposition is a vital research component in flow assurance requiring the accurate description of the phenomena under various operational conditions. The structure of asphaltene molecules and the presence of heteroatoms play a significant role in the intermolecular forces and the mechanism of asphaltene aggregation. Nevertheless, the intermolecular forces, e.g., polar forces, and their addition to thermodynamic modeling of asphaltene phase behavior still need investigation. While the traditional equation of state (EoS), e.g., cubic EoS, does not provide any special treatment to polar energy, the π-π interaction and polar effect can be mapped into the EoS using a separate polar term. In this research, we use cubic EoS, cubic plus polar (CPP) EoS, and molecular dynamics (MD) (three different modeling approaches) to analyze the effect of asphaltene structure and operational conditions on the precipitation phenomenon. Comparing the error associated with correlation and prediction results of the models, we show that the CPP approach using optimization to tune parameters of the EoS is the most reliable approach, followed by CPP EoS using MD to find dipole moment for the aryl-linked core asphaltene structure. The CPP EoS and MD optimizing island structure for asphaltene is the third-best model, and SRK EoS is a less efficient approach. Considering the values for dipole moment and molecular weight of asphaltene, along with correlation and prediction ability of the techniques, it is revealed that polar forces can be considered in a separate term in addition to van der Waals force to increase the model efficiency. Moreover, the aryl structure with a 750 g/mol molecular weight and one/two thiophene/pyridine group is the most proper asphaltene structure.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88015718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To reduce power usage and wear and tear on pumping equipment, low producing wells are often placed on timers. This study demonstrates how timer settings can be rapidly and effectively optimized using detailed measurement of tubing pressure. Pressure monitoring devices, based on an internet of things (IoT) architecture, were installed on the flow-t of 24 pumping wells, gathered and stored per second pressure data, and used it to establish optimal timer settings.
{"title":"Pump Jack Timer Optimization Using Detailed, High-Quality Pressure Monitoring","authors":"R. Gordon","doi":"10.2118/208964-ms","DOIUrl":"https://doi.org/10.2118/208964-ms","url":null,"abstract":"\u0000 To reduce power usage and wear and tear on pumping equipment, low producing wells are often placed on timers. This study demonstrates how timer settings can be rapidly and effectively optimized using detailed measurement of tubing pressure. Pressure monitoring devices, based on an internet of things (IoT) architecture, were installed on the flow-t of 24 pumping wells, gathered and stored per second pressure data, and used it to establish optimal timer settings.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91221500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. Hamdi, Raja Zuhaili Aimran Raja Zainal Raffik, O. Talabi, U. Patel, E. Mackay, M. Bataee
The rise in global warming is due to the high emissions of greenhouse gases (GHG) around the world. Carbon dioxide (CO2) gas emissions, a by-product from the petroleum industry, is contributors to climate change. One technology that may help curb CO2 gas emissions is injecting the gas into the subsurface reservoir. In this study, CO2 mineral trapping behaviour and its reactions within a wet basaltic rock containing Olivine mineral are captured and simulated in a full field numerical simulation model. A 2-stage approach was planned to develop the full field numerical model. In the first stage, a single cell model was developed, assessed and matched to the literature experiments with several assumptions considered and applied. Following this, the second stage involved developing a full field model to observe and analyse the distribution and concentration of CO2 during injection, as well as its sequestration as a solid phase (i.e., mineral trapping). The overall volume ratio of injected CO2 versus water was also assessed to ensure enough CO2 were injected into the basalt rock ensuring clear distribution of CO2 in the rock either in dissolved, trapped, or mobile state. In this study, the injected volume covered 4% of the total water volume. Results show that mineralization occurs faster than expected when CO2 gas was introduced to the wet basaltic rock especially near the CO2 injector wellbore. The mineralization speed depends on the reaction rate, modelling (cell) surface area and volume as well as the reaction rate coefficient where it was tuned to match the experimental results. The time required for the CO2 component to travel within the rock was also assessed to give a clear picture of the CO2 distribution where it took 10 years to reach 1000 ft away from the injector wellbore within a 440 ft thick reservoir.
{"title":"CO2 Mineral Trapping in Basaltic Formation During CO2 Storage","authors":"Z. Hamdi, Raja Zuhaili Aimran Raja Zainal Raffik, O. Talabi, U. Patel, E. Mackay, M. Bataee","doi":"10.2118/208935-ms","DOIUrl":"https://doi.org/10.2118/208935-ms","url":null,"abstract":"\u0000 The rise in global warming is due to the high emissions of greenhouse gases (GHG) around the world. Carbon dioxide (CO2) gas emissions, a by-product from the petroleum industry, is contributors to climate change. One technology that may help curb CO2 gas emissions is injecting the gas into the subsurface reservoir. In this study, CO2 mineral trapping behaviour and its reactions within a wet basaltic rock containing Olivine mineral are captured and simulated in a full field numerical simulation model. A 2-stage approach was planned to develop the full field numerical model. In the first stage, a single cell model was developed, assessed and matched to the literature experiments with several assumptions considered and applied. Following this, the second stage involved developing a full field model to observe and analyse the distribution and concentration of CO2 during injection, as well as its sequestration as a solid phase (i.e., mineral trapping). The overall volume ratio of injected CO2 versus water was also assessed to ensure enough CO2 were injected into the basalt rock ensuring clear distribution of CO2 in the rock either in dissolved, trapped, or mobile state. In this study, the injected volume covered 4% of the total water volume. Results show that mineralization occurs faster than expected when CO2 gas was introduced to the wet basaltic rock especially near the CO2 injector wellbore. The mineralization speed depends on the reaction rate, modelling (cell) surface area and volume as well as the reaction rate coefficient where it was tuned to match the experimental results. The time required for the CO2 component to travel within the rock was also assessed to give a clear picture of the CO2 distribution where it took 10 years to reach 1000 ft away from the injector wellbore within a 440 ft thick reservoir.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88529166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maximizing the hydrocarbon recovery for a given unconventional asset often clashes well spacing considerations against completion design. This can result in complex child/parent well interactions that can include frac-hits and reserve reallocation. In planning for a successful field development within the vicinity of producing assets, the risk of frac-hits must be evaluated carefully to minimize any damage and potential profit loss. Multiple factors such as depletion, well spacing, rock properties, and completion design all contribute to the risk of a frac-hit. Understanding the probable cause of a frac-hit allows for appropriate mitigation operations such as parent well pre-loading, re-fracturing, increased offset spacing, and revised completion design to be considered. To evaluate each remedial operation, several unconventional Permian oil wells were studied with Rate Transient Analysis (RTA) to identify well and reservoir characteristics. Based on these results multi-phase/multi-well numerical modeling was performed to evaluate how effective each operation would be to minimize the risk of future frac-hits. Simulation results regarding pressure and production performance of parent/child wells will be presented applying different frac-hit mitigation methods. Pressure build-ups around the wellbore were determined considering parent well shut-in; gas injection and water injection (pre-loading). Water injection resulted in the highest pressure build-ups in the vicinity of the wellbore (which reduces the risk of a frac-hit); however, it takes several months to unload the injected water. Production uplift due to a re-fracturing operation was evaluated with numerical modeling assuming different fracture designs that include the extension of current hydraulic fractures and adding new fractures. The performance of child and parent wells were then investigated by changing the well spacing and completion. Optimum combinations of well spacing and completion designs were determined to maximize child/parent well production and minimize the risk of frac-hits. Finally, the impact of parent well depletion on the productivity of the child well is determined. This work presents a replicable and accessible workflow to assess the impact of multiple frac-hit mitigation methods on reservoir performance.
{"title":"Child/Parent Well Interactions; Study the Solutions to Prevent Frac-Hits","authors":"A. Haghighat, James Ewert","doi":"10.2118/208934-ms","DOIUrl":"https://doi.org/10.2118/208934-ms","url":null,"abstract":"\u0000 Maximizing the hydrocarbon recovery for a given unconventional asset often clashes well spacing considerations against completion design. This can result in complex child/parent well interactions that can include frac-hits and reserve reallocation. In planning for a successful field development within the vicinity of producing assets, the risk of frac-hits must be evaluated carefully to minimize any damage and potential profit loss.\u0000 Multiple factors such as depletion, well spacing, rock properties, and completion design all contribute to the risk of a frac-hit. Understanding the probable cause of a frac-hit allows for appropriate mitigation operations such as parent well pre-loading, re-fracturing, increased offset spacing, and revised completion design to be considered. To evaluate each remedial operation, several unconventional Permian oil wells were studied with Rate Transient Analysis (RTA) to identify well and reservoir characteristics. Based on these results multi-phase/multi-well numerical modeling was performed to evaluate how effective each operation would be to minimize the risk of future frac-hits.\u0000 Simulation results regarding pressure and production performance of parent/child wells will be presented applying different frac-hit mitigation methods. Pressure build-ups around the wellbore were determined considering parent well shut-in; gas injection and water injection (pre-loading). Water injection resulted in the highest pressure build-ups in the vicinity of the wellbore (which reduces the risk of a frac-hit); however, it takes several months to unload the injected water. Production uplift due to a re-fracturing operation was evaluated with numerical modeling assuming different fracture designs that include the extension of current hydraulic fractures and adding new fractures. The performance of child and parent wells were then investigated by changing the well spacing and completion. Optimum combinations of well spacing and completion designs were determined to maximize child/parent well production and minimize the risk of frac-hits. Finally, the impact of parent well depletion on the productivity of the child well is determined.\u0000 This work presents a replicable and accessible workflow to assess the impact of multiple frac-hit mitigation methods on reservoir performance.","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72713053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Lower Cretaceous McMurray Formation in the Athabasca Oil Sands consists of channel belt deposits formed from meandering river systems. Large-scale fluvial point bars and other components of meander-belts compose this heterogeneous formation and are the source of complex sedimentary facies relationships. Recognition and correct interpretation of the spatial facies distribution, hence connectivity of the reservoir system, is essential to optimal field development and project economics. It is, therefore, crucial to understand river depositional processes, link associated facies to connectivity metrics, and implement them in flow modelling for hydrocarbon exploration. In the geological modelling phase, we analyzed data collected through high-density drilling, extensive coring, and three-dimensional (3D) seismic to map the internal stratigraphic architecture for different reservoir levels. The model captures the 3D representation of different depositional elements, including point bars, counter point bars, side bars, and abandoned channel fills. The deterministic interpretations constrain the stochastic simulation of the reservoir parameters, and distinct morphology, facies associations, and reservoir potential characterize the zones. Our workflow improves the geological realism of subsurface models and allows quantitative analysis of the spatial uncertainty. Including depositional bedding geometries in the modelling helps reduce uncertainties in net continuous bitumen estimations. It improves the knowledge of reservoir connectivity and compartmentalization. The ultra-defined model provides the framework for detailed analysis and optimal field development. This paper presents a new computationally efficient measure for connectivity based on detailed geological interpretations and mapping inclined heterolithic strata (IHS) in point bar deposits. In the calculations, we account for: facies distributions, porosity, permeability along the principal flow axis, and oil saturation,pressure and elevation (potential energy gradients),well locations, andtortuosity of the fluid flow streamlines. To evaluate the effect of sedimentary heterogeneities on key reservoir performance indicators, we formulate the reservoir connectivity as a mathematical optimization problem and estimate the flux in the connected porosity. Applying the methodology on a point-bar deposit shows that the connectivity factor strongly correlates with the ensuing recovery responses. This novel, computationally inexpensive approach captures the uncertainty in reservoir rock distributions and provides a quick and practical measurement for decision-making in reservoir management problems. Its features enable evaluating multiple reservoir parameters and using Monte Carlo techniques to quantify uncertainty and risk propagation in the presence of geological uncertainty to rank field portfolios. In the SAGD examples, the method estimates steam chamber development and conformance with high c
{"title":"Measuring Connectivity in Complex Reservoirs: Implications for Oil Sands Development","authors":"S. Nejadi, Stephen M. Hubbard","doi":"10.2118/208927-ms","DOIUrl":"https://doi.org/10.2118/208927-ms","url":null,"abstract":"\u0000 The Lower Cretaceous McMurray Formation in the Athabasca Oil Sands consists of channel belt deposits formed from meandering river systems. Large-scale fluvial point bars and other components of meander-belts compose this heterogeneous formation and are the source of complex sedimentary facies relationships. Recognition and correct interpretation of the spatial facies distribution, hence connectivity of the reservoir system, is essential to optimal field development and project economics. It is, therefore, crucial to understand river depositional processes, link associated facies to connectivity metrics, and implement them in flow modelling for hydrocarbon exploration.\u0000 In the geological modelling phase, we analyzed data collected through high-density drilling, extensive coring, and three-dimensional (3D) seismic to map the internal stratigraphic architecture for different reservoir levels. The model captures the 3D representation of different depositional elements, including point bars, counter point bars, side bars, and abandoned channel fills. The deterministic interpretations constrain the stochastic simulation of the reservoir parameters, and distinct morphology, facies associations, and reservoir potential characterize the zones. Our workflow improves the geological realism of subsurface models and allows quantitative analysis of the spatial uncertainty. Including depositional bedding geometries in the modelling helps reduce uncertainties in net continuous bitumen estimations. It improves the knowledge of reservoir connectivity and compartmentalization. The ultra-defined model provides the framework for detailed analysis and optimal field development.\u0000 This paper presents a new computationally efficient measure for connectivity based on detailed geological interpretations and mapping inclined heterolithic strata (IHS) in point bar deposits. In the calculations, we account for: facies distributions, porosity, permeability along the principal flow axis, and oil saturation,pressure and elevation (potential energy gradients),well locations, andtortuosity of the fluid flow streamlines.\u0000 To evaluate the effect of sedimentary heterogeneities on key reservoir performance indicators, we formulate the reservoir connectivity as a mathematical optimization problem and estimate the flux in the connected porosity.\u0000 Applying the methodology on a point-bar deposit shows that the connectivity factor strongly correlates with the ensuing recovery responses. This novel, computationally inexpensive approach captures the uncertainty in reservoir rock distributions and provides a quick and practical measurement for decision-making in reservoir management problems. Its features enable evaluating multiple reservoir parameters and using Monte Carlo techniques to quantify uncertainty and risk propagation in the presence of geological uncertainty to rank field portfolios. In the SAGD examples, the method estimates steam chamber development and conformance with high c","PeriodicalId":11077,"journal":{"name":"Day 2 Thu, March 17, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73127782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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