Drilling and fracturing are considered to be one of the major costs in the oil and gas industry. Cost may reach tens of millions of dollars and improper design may lead to significant loss of money and time. Reliable fracturing and drilling designs are governed with decent and representative rock mechanical properties. Such properties are measured mainly by analyzing multiple previously cored wells in the same formation. The nature of the conducted tests on the collected plugs are destructive and samples cannot be restored after performing the rock mechanical testing. This may disable further evaluation on the same plugs. This study aims to build an artificial neural network (ANN) model that is capable of predicting the main rock mechanical properties, such as Poisson's ratio and compressive strength from already available lab and field measurements. The log data will be combined together with preliminary lab rock properties to build a smart model capable of predicting advance rock mechanical properties. Hence, the model will provide initial rock mechanical properties that are estimated almost immediately and without undergoing costly and timely rock mechanical laboratory tests. The study will also give an advantage to performing preliminary estimates of such parameters without the need for destructive mechanical core testing. The ultimate goal is to draw a full field geomechanical mapping with this tool rather than having localized scattered data. The AI tool will be trained utilizing representative sets of rock mechanical data with multiple feed-forward backpropagation learning techniques. The study will help in localizing future well location and optimizing multi-stage fracturing designs. These produced data are needed for upstream applications such as wellbore stability, sanding tendency, hydraulic fracturing, and horizontal/multi-lateral drilling.
{"title":"Geomechanical Properties Estimation Utilizing Artificial Intelligence Prediction Tool","authors":"M. Alabbad, M. Alqam, Hussain Aljeshi","doi":"10.2118/204672-ms","DOIUrl":"https://doi.org/10.2118/204672-ms","url":null,"abstract":"\u0000 Drilling and fracturing are considered to be one of the major costs in the oil and gas industry. Cost may reach tens of millions of dollars and improper design may lead to significant loss of money and time. Reliable fracturing and drilling designs are governed with decent and representative rock mechanical properties. Such properties are measured mainly by analyzing multiple previously cored wells in the same formation. The nature of the conducted tests on the collected plugs are destructive and samples cannot be restored after performing the rock mechanical testing. This may disable further evaluation on the same plugs.\u0000 This study aims to build an artificial neural network (ANN) model that is capable of predicting the main rock mechanical properties, such as Poisson's ratio and compressive strength from already available lab and field measurements. The log data will be combined together with preliminary lab rock properties to build a smart model capable of predicting advance rock mechanical properties. Hence, the model will provide initial rock mechanical properties that are estimated almost immediately and without undergoing costly and timely rock mechanical laboratory tests. The study will also give an advantage to performing preliminary estimates of such parameters without the need for destructive mechanical core testing.\u0000 The ultimate goal is to draw a full field geomechanical mapping with this tool rather than having localized scattered data. The AI tool will be trained utilizing representative sets of rock mechanical data with multiple feed-forward backpropagation learning techniques. The study will help in localizing future well location and optimizing multi-stage fracturing designs. These produced data are needed for upstream applications such as wellbore stability, sanding tendency, hydraulic fracturing, and horizontal/multi-lateral drilling.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89355893","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}
Extensive computing resources are required to leverage todays advanced geoscience workflows that are used to explore and characterize giant petroleum resources. In these cases, high-performance workstations are often unable to adequately handle the scale of computing required. The workflows typically utilize complex and massive data sets, which require advanced computing resources to store, process, manage, and visualize various forms of the data throughout the various lifecycles. This work describes a large-scale geoscience end-to-end interpretation platform customized to run on a cluster-based remote visualization environment. A team of computing infrastructure and geoscience workflow experts was established to collaborate on the deployment, which was broken down into separate phases. Initially, an evaluation and analysis phase was conducted to analyze computing requirements and assess potential solutions. A testing environment was then designed, implemented and benchmarked. The third phase used the test environment to determine the scale of infrastructure required for the production environment. Finally, the full-scale customized production environment was deployed for end users. During testing phase, aspects such as connectivity, stability, interactivity, functionality, and performance were investigated using the largest available geoscience datasets. Multiple computing configurations were benchmarked until optimal performance was achieved, under applicable corporate information security guidelines. It was observed that the customized production environment was able to execute workflows that were unable to run on local user workstations. For example, while conducting connectivity, stability and interactivity benchmarking, the test environment was operated for extended periods to ensure stability for workflows that require multiple days to run. To estimate the scale of the required production environment, varying categories of users’ portfolio were determined based on data type, scale and workflow. Continuous monitoring of system resources and utilization enabled continuous improvements to the final solution. The utilization of a fit-for-purpose, customized remote visualization solution may reduce or ultimately eliminate the need to deploy high-end workstations to all end users. Rather, a shared, scalable and reliable cluster-based solution can serve a much larger user community in a highly performant manner.
{"title":"Integrated Cloud Computing Environment for Upstream Geoscience Workflows","authors":"M. Al-Habib, Yasser Al-Ghamdi","doi":"10.2118/204848-ms","DOIUrl":"https://doi.org/10.2118/204848-ms","url":null,"abstract":"\u0000 Extensive computing resources are required to leverage todays advanced geoscience workflows that are used to explore and characterize giant petroleum resources. In these cases, high-performance workstations are often unable to adequately handle the scale of computing required. The workflows typically utilize complex and massive data sets, which require advanced computing resources to store, process, manage, and visualize various forms of the data throughout the various lifecycles. This work describes a large-scale geoscience end-to-end interpretation platform customized to run on a cluster-based remote visualization environment.\u0000 A team of computing infrastructure and geoscience workflow experts was established to collaborate on the deployment, which was broken down into separate phases. Initially, an evaluation and analysis phase was conducted to analyze computing requirements and assess potential solutions. A testing environment was then designed, implemented and benchmarked. The third phase used the test environment to determine the scale of infrastructure required for the production environment. Finally, the full-scale customized production environment was deployed for end users.\u0000 During testing phase, aspects such as connectivity, stability, interactivity, functionality, and performance were investigated using the largest available geoscience datasets. Multiple computing configurations were benchmarked until optimal performance was achieved, under applicable corporate information security guidelines.\u0000 It was observed that the customized production environment was able to execute workflows that were unable to run on local user workstations. For example, while conducting connectivity, stability and interactivity benchmarking, the test environment was operated for extended periods to ensure stability for workflows that require multiple days to run.\u0000 To estimate the scale of the required production environment, varying categories of users’ portfolio were determined based on data type, scale and workflow. Continuous monitoring of system resources and utilization enabled continuous improvements to the final solution.\u0000 The utilization of a fit-for-purpose, customized remote visualization solution may reduce or ultimately eliminate the need to deploy high-end workstations to all end users. Rather, a shared, scalable and reliable cluster-based solution can serve a much larger user community in a highly performant manner.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"92 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79503759","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}
E. Gurina, Ksenia Antipova, Nikita Klyuchnikov, D. Koroteev
Drilling accidents prediction is the important task in well construction. Drilling support software allows observing the drilling parameters for multiple wells at the same time and artificial intelligence helps detecting the drilling accident predecessor ahead the emergency situation. We present machine learning (ML) algorithm for prediction of such accidents as stuck, mud loss, fluid show, washout, break of drill string and shale collar. The model for forecasting the drilling accidents is based on the "Bag-of-features" approach, which implies the use of distributions of the directly recorded data as the main features. Bag-of-features implies the labeling of small parts of data by the particular symbol, named codeword. Building histograms of symbols for the data segment, one could use the histogram as an input for the machine learning algorithm. Fragments of real-time mud log data were used to create the model. We define more than 1000 drilling accident predecessors for more than 60 real accidents and about 2500 normal drilling cases as a training set for ML model. The developed model analyzes real-time mud log data and calculates the probability of accident. The result is presented as a probability curve for each type of accident; if the critical probability value is exceeded, the user is notified of the risk of an accident. The Bag-of-features model shows high performance by validation both on historical data and in real time. The prediction quality does not vary field to field and could be used in different fields without additional training of the ML model. The software utilizing the ML model has microservice architecture and is integrated with the WITSML data server. It is capable of real-time accidents forecasting without human intervention. As a result, the system notifies the user in all cases when the situation in the well becomes similar to the pre-accident one, and the engineer has enough time to take the necessary actions to prevent an accident.
{"title":"Machine Learning Microservice for Identification of Accident Predecessors","authors":"E. Gurina, Ksenia Antipova, Nikita Klyuchnikov, D. Koroteev","doi":"10.2118/204707-ms","DOIUrl":"https://doi.org/10.2118/204707-ms","url":null,"abstract":"\u0000 Drilling accidents prediction is the important task in well construction. Drilling support software allows observing the drilling parameters for multiple wells at the same time and artificial intelligence helps detecting the drilling accident predecessor ahead the emergency situation. We present machine learning (ML) algorithm for prediction of such accidents as stuck, mud loss, fluid show, washout, break of drill string and shale collar.\u0000 The model for forecasting the drilling accidents is based on the \"Bag-of-features\" approach, which implies the use of distributions of the directly recorded data as the main features. Bag-of-features implies the labeling of small parts of data by the particular symbol, named codeword. Building histograms of symbols for the data segment, one could use the histogram as an input for the machine learning algorithm. Fragments of real-time mud log data were used to create the model. We define more than 1000 drilling accident predecessors for more than 60 real accidents and about 2500 normal drilling cases as a training set for ML model.\u0000 The developed model analyzes real-time mud log data and calculates the probability of accident. The result is presented as a probability curve for each type of accident; if the critical probability value is exceeded, the user is notified of the risk of an accident. The Bag-of-features model shows high performance by validation both on historical data and in real time. The prediction quality does not vary field to field and could be used in different fields without additional training of the ML model.\u0000 The software utilizing the ML model has microservice architecture and is integrated with the WITSML data server. It is capable of real-time accidents forecasting without human intervention. As a result, the system notifies the user in all cases when the situation in the well becomes similar to the pre-accident one, and the engineer has enough time to take the necessary actions to prevent an accident.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"124 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77570290","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}
It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works present valid and reliable results, they are expensive and time consuming. On the other hand, continuous and regular determination of the rheological mud properties can perform its essential functions during well construction. More uncertainties in planning the drilling fluid properties meant that more challenges may be exposed during drilling operations. This study presents two predictive techniques, multiple regression analysis (MRA) and artificial neural networks (ANNs), to determine the rheological properties of water-based drilling fluid based on other simple measurable properties. While mud density (MW), marsh funnel (MF), and solid% are key input parameters in this study, the output functions or models are plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength. The prediction methods were demonstrated by means of a field case in eastern Iraq, using datasets from daily drilling reports of two wells in addition to the laboratory measurements. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error RMSE) have been used in this study. The current results of this study support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. However, a scattering around each fit curve is observed which proved that one rheological property alone is not sufficient to estimate other properties. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high performance capacity have been developed in this study to determine the rheological fluid properties based on simple and quick equipment as mud balance and marsh funnel. This study presents cost-effective models to determine the rheological fluid properties for future well planning in Iraqi oil fields.
{"title":"Development of New Models to Determine the Rheological Parameters of Water-based Drilling Fluid Using Artificial Intelligence","authors":"F. Hadi, A. Noori, H. Hussein, Ameer Khudhair","doi":"10.2118/204597-ms","DOIUrl":"https://doi.org/10.2118/204597-ms","url":null,"abstract":"\u0000 It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works present valid and reliable results, they are expensive and time consuming. On the other hand, continuous and regular determination of the rheological mud properties can perform its essential functions during well construction. More uncertainties in planning the drilling fluid properties meant that more challenges may be exposed during drilling operations.\u0000 This study presents two predictive techniques, multiple regression analysis (MRA) and artificial neural networks (ANNs), to determine the rheological properties of water-based drilling fluid based on other simple measurable properties. While mud density (MW), marsh funnel (MF), and solid% are key input parameters in this study, the output functions or models are plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength. The prediction methods were demonstrated by means of a field case in eastern Iraq, using datasets from daily drilling reports of two wells in addition to the laboratory measurements. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error RMSE) have been used in this study.\u0000 The current results of this study support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. However, a scattering around each fit curve is observed which proved that one rheological property alone is not sufficient to estimate other properties. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high performance capacity have been developed in this study to determine the rheological fluid properties based on simple and quick equipment as mud balance and marsh funnel. This study presents cost-effective models to determine the rheological fluid properties for future well planning in Iraqi oil fields.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74442677","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}
Unconventional shale reservoirs are characterized by low porosity and ultra-low permeability. Natural fractures are known to be present and considered a critical factor for the enhanced post-stimulation productivity. Accounting for natural fractures with existing techniques has not been widely adopted owing to their complexity or lack of validation. Ongoing research efforts are striving to understand how natural fractures can be accounted for and accurately modeled in fluid flow of the subject reservoirs. This study utilized Eagle Ford well data comprising reservoir properties, stimulation metrics, production, microseismicity and permeability measurements from a core plug. The methodology comprised use of production data to extract a linear flow regime parameter. This was coupled with fracture geometry, predicted from hydraulic fracture modeling and microseismicity, to estimate the system permeability. From interpreting microseismic events as slips on critically stressed natural fractures, explicit modeling incorporating a discrete fracture network (DFN) assumed activated natural fractures supplement conductive reservoir contact area. Thus, allowed the estimation of matrix permeability. For validation, the aforementioned was compared with core plug permeability measurements. Results from modeling of planar hydraulic fractures, with microseismicity as validation, predicted planar fracture geometry which when coupled with the linear flow parameter resulted in a system permeability. Incorporation of DFNs to account for activated natural fractures yielded matrix permeability in picodarcy range. A review of laboratory permeability measurements exhibited stress dependence with the value at the maximum experimental confining pressure of 4000 psi in the same range as the computed system permeability. However, the confining pressures used in the experiments were less than the in situ effective stress. Correction for representative stress yielded an ultra-low matrix permeability in the same range as the DFN-based picodarcy matrix permeability. Thus, supporting the adopted drainage architecture and often suggested role of natural fractures in shale reservoir fluid flow. This study presents a multi-discipline workflow to account for natural fractures, and contributes to understanding that will improve laboratory petrophysics and the overall reservoir characterization of the subject reservoirs. Given that the Eagle Ford is an analogue of emerging shales elsewhere, results from this study can be widely adopted.
{"title":"Integration of Geoscience and Engineering Concepts to Account for Natural Fractures in Fluid Flow within Shale Reservoirs","authors":"Clay Kurison, A. Hakami, S. Kuleli","doi":"10.2118/204747-ms","DOIUrl":"https://doi.org/10.2118/204747-ms","url":null,"abstract":"\u0000 Unconventional shale reservoirs are characterized by low porosity and ultra-low permeability. Natural fractures are known to be present and considered a critical factor for the enhanced post-stimulation productivity. Accounting for natural fractures with existing techniques has not been widely adopted owing to their complexity or lack of validation. Ongoing research efforts are striving to understand how natural fractures can be accounted for and accurately modeled in fluid flow of the subject reservoirs. This study utilized Eagle Ford well data comprising reservoir properties, stimulation metrics, production, microseismicity and permeability measurements from a core plug. The methodology comprised use of production data to extract a linear flow regime parameter. This was coupled with fracture geometry, predicted from hydraulic fracture modeling and microseismicity, to estimate the system permeability. From interpreting microseismic events as slips on critically stressed natural fractures, explicit modeling incorporating a discrete fracture network (DFN) assumed activated natural fractures supplement conductive reservoir contact area. Thus, allowed the estimation of matrix permeability. For validation, the aforementioned was compared with core plug permeability measurements. Results from modeling of planar hydraulic fractures, with microseismicity as validation, predicted planar fracture geometry which when coupled with the linear flow parameter resulted in a system permeability. Incorporation of DFNs to account for activated natural fractures yielded matrix permeability in picodarcy range. A review of laboratory permeability measurements exhibited stress dependence with the value at the maximum experimental confining pressure of 4000 psi in the same range as the computed system permeability. However, the confining pressures used in the experiments were less than the in situ effective stress. Correction for representative stress yielded an ultra-low matrix permeability in the same range as the DFN-based picodarcy matrix permeability. Thus, supporting the adopted drainage architecture and often suggested role of natural fractures in shale reservoir fluid flow. This study presents a multi-discipline workflow to account for natural fractures, and contributes to understanding that will improve laboratory petrophysics and the overall reservoir characterization of the subject reservoirs. Given that the Eagle Ford is an analogue of emerging shales elsewhere, results from this study can be widely adopted.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74540673","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. Al-Taq, Abdulla A. Alrustum, Basil M. Alfakher, Hussain Al-Ibrahim
It is challenging to control water production in horizontal wells or in vertical wells having oil and water produced from the same zone using conventional methods such as through-tubing bridge plugs or other mechanical means. Relative permeability modifiers (RPMs), known to selectively reduce the relative permeability to water with minimum impact on the relative permeability to oil or gas, are considered a promising technology for solving this problem. The current generation of RPMs, unlike the old ones, can tolerate high hardness and so have higher success rates. An extensive experimental work was carried out to evaluate three RPMs for water control in gas and oil wells. Test conditions included gas flow in sandstone cores with temperatures of up to 300°F, and oil flow in carbonate cores with temperatures as high as 220°F. The effect of initial core permeability to brine, RPM concentration, flow rate, and water-wetting surfactants on the effectiveness of RPM to reduce water production was investigated using sandstone and carbonate cores. Coreflood experiments were undertaken at downhole conditions. The end-point relative permeabilities to various phases were measured. A back pressure of 500 psi, an overburden pressure of 3,500 to 5,000 psi and flow rates of 0.1 to 5 cm3/min were used. The concentration of RPM polymers was monitored in the core effluent using total organic carbon (TOC) analyzer to determine polymer retention in the core. The results revealed that temperature adversely affected the effectiveness of all RPMs evaluated. A better reduction in permeability to water was obtained at 220°F compared to that obtained at 300°F. The use of RPM at the right concentrations was found to significantly reduce permeability to water. A better water reduction was obtained at higher polymer injection rates, which was attributed to flow-induced polymer retention. Adsorption of RPM polymer tended to alter wettability of a carbonate rock to more water-wet. This paper discusses the effects of the above parameters on the performance of RPM in sandstone and carbonate reservoirs, and it gives some recommendations for improving the success rate of these chemical applications in the field.
{"title":"Relative Permeability Modifiers as a Chemical Means to Control Water Production in Oil and Gas Reservoirs","authors":"A. Al-Taq, Abdulla A. Alrustum, Basil M. Alfakher, Hussain Al-Ibrahim","doi":"10.2118/204692-ms","DOIUrl":"https://doi.org/10.2118/204692-ms","url":null,"abstract":"\u0000 It is challenging to control water production in horizontal wells or in vertical wells having oil and water produced from the same zone using conventional methods such as through-tubing bridge plugs or other mechanical means. Relative permeability modifiers (RPMs), known to selectively reduce the relative permeability to water with minimum impact on the relative permeability to oil or gas, are considered a promising technology for solving this problem. The current generation of RPMs, unlike the old ones, can tolerate high hardness and so have higher success rates.\u0000 An extensive experimental work was carried out to evaluate three RPMs for water control in gas and oil wells. Test conditions included gas flow in sandstone cores with temperatures of up to 300°F, and oil flow in carbonate cores with temperatures as high as 220°F. The effect of initial core permeability to brine, RPM concentration, flow rate, and water-wetting surfactants on the effectiveness of RPM to reduce water production was investigated using sandstone and carbonate cores.\u0000 Coreflood experiments were undertaken at downhole conditions. The end-point relative permeabilities to various phases were measured. A back pressure of 500 psi, an overburden pressure of 3,500 to 5,000 psi and flow rates of 0.1 to 5 cm3/min were used. The concentration of RPM polymers was monitored in the core effluent using total organic carbon (TOC) analyzer to determine polymer retention in the core.\u0000 The results revealed that temperature adversely affected the effectiveness of all RPMs evaluated. A better reduction in permeability to water was obtained at 220°F compared to that obtained at 300°F. The use of RPM at the right concentrations was found to significantly reduce permeability to water. A better water reduction was obtained at higher polymer injection rates, which was attributed to flow-induced polymer retention. Adsorption of RPM polymer tended to alter wettability of a carbonate rock to more water-wet. This paper discusses the effects of the above parameters on the performance of RPM in sandstone and carbonate reservoirs, and it gives some recommendations for improving the success rate of these chemical applications in the field.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79772287","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}
P. Luo, J. Harrist, Rabah Mesdour, Nathan Stmichel
Natural gas is sampled or produced throughout the lifespan of a field, including geochemical surface survey, mud gas logging, formation and well testing, and production. Detecting and measuring gas is a common practice in many upstream operations, providing gas composition and isotope data for multiple purposes, such as gas show, petroleum system analysis, fluid characterization, and production monitoring. Onsite gas analysis is usually conducted within a mud gas unit, which is operationally unavailable after drilling. Gas samples need be taken from the field and shipped back to laboratory for gas chromatography and isotope-ratio mass spectrometry analyses. Results take a considerable time and lack the resolution needed to fully characterize the heterogeneity and dynamics of fluids within the reservoir. We are developing and testing advanced sensing technology to move gas composition and isotope analyses to field for near real-time and onsite fluid characterization and monitoring. We have developed a novel QEPAS (quartz-enhanced photoacoustic spectroscopy) sensor system, employing a single interband cascade laser, to measure concentrations of methane (C1), ethane (C2), and propane (C3) in gas phase. The quartz fork detection module, laser driver, and interface are integrated as a small sensing box. The sensor, sample preparation enclosures and a computer are mounted in a rack as a gas analyzer prototype for the bench testing for oil industry application. Software is designed for monitoring sample preparation, collecting data, calibration and continuous reporting sample pressure and concentration data. The sensor achieved an ultimate detection limit of 90 ppb (parts per billion), 7 ppb and 3 ppm (parts per million) for C1, C2, and C3, respectively, for one second integration time. The detection limit for C2 made a record for QEPAS technique, and measuring C3 added a new capability to the technique. However, the linearity of the QEPAS sensing were previously reported in the range of 0 to 1000 ppm, which is mainly for trace gas detection. In the study, the prototype was separately tested on standard C1, C2, and C3 with different concentrations diluted in dry nitrogen (N2). Good linearity was obtained for all single components and the ranges of linearity were expanded to their typical concentrations (per cent, %) in natural gas samples from oil and gas fields. The testing on the C1-C2 mixtures confirms that accurate C1 and C2 concentrations in % level can be achieved by the prototype. The testing results on C1-C2-C3 mixtures demonstrate the capability of simultaneous detection of three hydrocarbon components and the probability to determine their precise concentrations by QEPAS sensing. This advancement of simultaneous measuring C1, C2 and C3 concentrations, with previously demonstrated capability for hydrogen sulfide (H2S) and carbon dioxide (CO2) and potential to analyze carbon isotopes (13C/12C), promotes QEPAS as a prominent optical technolo
{"title":"Moving Gas Geochemical Analysis from Lab to Field by Advanced Gas Sensor for Onsite Fluid Characterization and Time-Lapse Monitoring","authors":"P. Luo, J. Harrist, Rabah Mesdour, Nathan Stmichel","doi":"10.2118/204775-ms","DOIUrl":"https://doi.org/10.2118/204775-ms","url":null,"abstract":"\u0000 Natural gas is sampled or produced throughout the lifespan of a field, including geochemical surface survey, mud gas logging, formation and well testing, and production. Detecting and measuring gas is a common practice in many upstream operations, providing gas composition and isotope data for multiple purposes, such as gas show, petroleum system analysis, fluid characterization, and production monitoring. Onsite gas analysis is usually conducted within a mud gas unit, which is operationally unavailable after drilling. Gas samples need be taken from the field and shipped back to laboratory for gas chromatography and isotope-ratio mass spectrometry analyses. Results take a considerable time and lack the resolution needed to fully characterize the heterogeneity and dynamics of fluids within the reservoir. We are developing and testing advanced sensing technology to move gas composition and isotope analyses to field for near real-time and onsite fluid characterization and monitoring.\u0000 We have developed a novel QEPAS (quartz-enhanced photoacoustic spectroscopy) sensor system, employing a single interband cascade laser, to measure concentrations of methane (C1), ethane (C2), and propane (C3) in gas phase. The quartz fork detection module, laser driver, and interface are integrated as a small sensing box. The sensor, sample preparation enclosures and a computer are mounted in a rack as a gas analyzer prototype for the bench testing for oil industry application. Software is designed for monitoring sample preparation, collecting data, calibration and continuous reporting sample pressure and concentration data.\u0000 The sensor achieved an ultimate detection limit of 90 ppb (parts per billion), 7 ppb and 3 ppm (parts per million) for C1, C2, and C3, respectively, for one second integration time. The detection limit for C2 made a record for QEPAS technique, and measuring C3 added a new capability to the technique. However, the linearity of the QEPAS sensing were previously reported in the range of 0 to 1000 ppm, which is mainly for trace gas detection. In the study, the prototype was separately tested on standard C1, C2, and C3 with different concentrations diluted in dry nitrogen (N2). Good linearity was obtained for all single components and the ranges of linearity were expanded to their typical concentrations (per cent, %) in natural gas samples from oil and gas fields. The testing on the C1-C2 mixtures confirms that accurate C1 and C2 concentrations in % level can be achieved by the prototype. The testing results on C1-C2-C3 mixtures demonstrate the capability of simultaneous detection of three hydrocarbon components and the probability to determine their precise concentrations by QEPAS sensing.\u0000 This advancement of simultaneous measuring C1, C2 and C3 concentrations, with previously demonstrated capability for hydrogen sulfide (H2S) and carbon dioxide (CO2) and potential to analyze carbon isotopes (13C/12C), promotes QEPAS as a prominent optical technolo","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79782151","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}
Kui Xu, Jonathan Stewart-Ayala, Steve Jackson, Benton Hutchinson, Christina Sanders, W. Jakubowski, Joanne Jardine, Rose Lehman
Amid concerns over negative the environmental impacts of offshore chemicals, Baker Hughes explored new chemistries to develop environmentally friendly kinetic hydrate inhibitors (KHI). Our efforts were focused on improving biodegradability and toxicity of KHIs to meet environmental protection requirements, as well as mitigating challenges in field applications. A novel KHI design with branched polymers containing sugar alcohol ester groups as linkages, was proposed and synthesized. The new KHI polymer demonstrated > 20% biodegradability and >100 mg/L toxicity to seawater algae, and it also exhibited competitive or even better KHI performance to traditional non-biodegradable KHI products. Additionally, new KHI showed improved stability in water/brine at elevated temperatures as compared to traditional KHI products, which might mitigate concerns on polymer deposition at high temperatures.
{"title":"Novel Eco-Friendly Kinetic Hydrate Inhibitors","authors":"Kui Xu, Jonathan Stewart-Ayala, Steve Jackson, Benton Hutchinson, Christina Sanders, W. Jakubowski, Joanne Jardine, Rose Lehman","doi":"10.2118/204779-ms","DOIUrl":"https://doi.org/10.2118/204779-ms","url":null,"abstract":"\u0000 Amid concerns over negative the environmental impacts of offshore chemicals, Baker Hughes explored new chemistries to develop environmentally friendly kinetic hydrate inhibitors (KHI). Our efforts were focused on improving biodegradability and toxicity of KHIs to meet environmental protection requirements, as well as mitigating challenges in field applications. A novel KHI design with branched polymers containing sugar alcohol ester groups as linkages, was proposed and synthesized. The new KHI polymer demonstrated > 20% biodegradability and >100 mg/L toxicity to seawater algae, and it also exhibited competitive or even better KHI performance to traditional non-biodegradable KHI products. Additionally, new KHI showed improved stability in water/brine at elevated temperatures as compared to traditional KHI products, which might mitigate concerns on polymer deposition at high temperatures.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83991959","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. Hanif, E. Frost, Fei Le, M. Nikitenko, Mikhail Blinov, N. Velker
Dielectric dispersion measurements are increasingly used by petrophysicists to reduce uncertainty in their hydrocarbon saturation analysis, and subsequent reserves estimation, especially when encountered with challenging environments. Some of these challenges are related to variable or unknown formation water salinity and/or a changing rock texture which is a common attribute of carbonate reservoirs found in the Middle East. A new multi-frequency, multi-spacing dielectric logging service, utilizes a sensor array scheme which provides wave attenuation and phase difference measurements at multiple depths of investigation up to 8 inches inside the formation. The improvement in depth of investigation provides a better measurement of true formation properties, however, also provides a higher likelihood of measuring radial heterogeneity due to spatially variable shallow mud-filtrate invasion. Meaningful petrophysical interpretation requires an accurate electromagnetic (EM) inversion, which accommodates this heterogeneity, while converting raw tool measurements to true formation dielectric properties. Forward modeling solvers are typically beset with a slow processing speed precluding use of complex, albeit representative, formation petrophysical models. An artificial neural network (ANN) has been trained to significantly speed up the forward solver, thus leading to implementation and real-time execution of a complex multi-layer radial inversion algorithm. The paper describes, in detail, the development, training and validation of both the ANN network and the inversion algorithm. The presented algorithm and ANN inversion has shown ability to accurately resolve mud filtrate invasion profile as well as the true formation properties of individual layers. Examples are presented which demonstrate that comprehensive, multi-frequency, multi-array, EM data sets are inverted efficiently for dis-similar dielectric properties of both invaded and non-invaded formation layers around the wellbore. The results are further utilized for accurate hydrocarbon quantification otherwise not achieved by conventional resistivity based saturation techniques. This paper presents the development of a new EM inversion algorithm and an artificial neural network (ANN) trained to significantly speed up the solution of this algorithm. This approach leads to a fast turnaround for an accurate petrophysical analysis, reserves estimate and completion decisions.
{"title":"A Fast ANN Trained Solver Enables Real-Time Radial Inversion of Dielectric Dispersion Data & Accurate Estimate of Reserves in Challenging Environments","authors":"A. Hanif, E. Frost, Fei Le, M. Nikitenko, Mikhail Blinov, N. Velker","doi":"10.2118/204904-ms","DOIUrl":"https://doi.org/10.2118/204904-ms","url":null,"abstract":"\u0000 Dielectric dispersion measurements are increasingly used by petrophysicists to reduce uncertainty in their hydrocarbon saturation analysis, and subsequent reserves estimation, especially when encountered with challenging environments. Some of these challenges are related to variable or unknown formation water salinity and/or a changing rock texture which is a common attribute of carbonate reservoirs found in the Middle East. A new multi-frequency, multi-spacing dielectric logging service, utilizes a sensor array scheme which provides wave attenuation and phase difference measurements at multiple depths of investigation up to 8 inches inside the formation. The improvement in depth of investigation provides a better measurement of true formation properties, however, also provides a higher likelihood of measuring radial heterogeneity due to spatially variable shallow mud-filtrate invasion. Meaningful petrophysical interpretation requires an accurate electromagnetic (EM) inversion, which accommodates this heterogeneity, while converting raw tool measurements to true formation dielectric properties.\u0000 Forward modeling solvers are typically beset with a slow processing speed precluding use of complex, albeit representative, formation petrophysical models. An artificial neural network (ANN) has been trained to significantly speed up the forward solver, thus leading to implementation and real-time execution of a complex multi-layer radial inversion algorithm. The paper describes, in detail, the development, training and validation of both the ANN network and the inversion algorithm.\u0000 The presented algorithm and ANN inversion has shown ability to accurately resolve mud filtrate invasion profile as well as the true formation properties of individual layers. Examples are presented which demonstrate that comprehensive, multi-frequency, multi-array, EM data sets are inverted efficiently for dis-similar dielectric properties of both invaded and non-invaded formation layers around the wellbore. The results are further utilized for accurate hydrocarbon quantification otherwise not achieved by conventional resistivity based saturation techniques.\u0000 This paper presents the development of a new EM inversion algorithm and an artificial neural network (ANN) trained to significantly speed up the solution of this algorithm. This approach leads to a fast turnaround for an accurate petrophysical analysis, reserves estimate and completion decisions.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85429558","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}
J. Franquet, A. N. Martin, Viraj Telaj, H. Khairy, A. Soliman, Roman Zabirov, Syofyan Syofvas, Andrey Nestyagin, T. Fauzi, N. Talib, T. Al-Shabibi, B. Banihammad
The objective of this work was to quantify the in-situ stress contrast between the reservoir and the surrounding dense carbonate layers above and below for accurate hydraulic fracturing propagation modelling and precise fracture containment prediction. The goal was to design an optimum reservoir stimulation treatment in a Lower Cretaceous tight oil reservoir without fracturing the lower dense zone and communicating the high-permeability reservoir below. This case study came from Abu Dhabi onshore where a vertical pilot hole was drilled to perform in-situ stress testing to design a horizontal multi-stage hydraulic fractured well in a 35-ft thick reservoir. The in-situ stress tests were obtained using a wireline straddle packer microfrac tool able to measure formation breakdown and fracture closure pressures in multiple zones across the dense and reservoir layers. Standard dual-packer micro-injection tests were conducted to measure stresses in reservoir layers while single-packer sleeve-frac tests were done to breakdown high-stress dense layers. The pressure versus time was monitored in real-time to make prompt geoscience decisions during the acquisition of the data. The formation breakdown and fracture closure pressures were utilized to calibrated minimum and maximum lateral tectonic strains for accurate in-situ stress profile. Then, the calibrated stress profile was used to simulate fracture propagation and containment for the subsequent reservoir stimulation design. A total 17 microfrac stress tests were completed in 13 testing points across the vertical pilot, 12 with dual-packer injection and 5 with single-packer sleeve fracturing inflation. The fracture closure results showed stronger stress contrast towards the lower dense zone (900 psi) in comparison with the upper dense zone (600 psi). These measurements enabled the oilfield operating company to place the lateral well in a lower section of the tight reservoir without the risk of fracturing out-of-zone. The novelty of this in-situ stress testing consisted of single packer inflations (sleeve frac) in an 8½-in hole in order to achieve higher differential pressures (7,000 psi) to breakdown the dense zones. The single packer breakdown permitted fracture propagation and reliable closure measurements with dual-packer injection at a lower differential reopening pressure (4,500 psi). Microfracturing the tight formation prior to fluid sampling produced clean oil samples with 80% reduction of pump out time in comparison to conventional straddle packer sampling operations. This was a breakthrough operational outcome in sampling this reservoir.
{"title":"Perfecting Straddle Packer Microfrac Stress Contrast Measurements for Hydraulic Fracturing Design in UAE Tight Oil Reservoir","authors":"J. Franquet, A. N. Martin, Viraj Telaj, H. Khairy, A. Soliman, Roman Zabirov, Syofyan Syofvas, Andrey Nestyagin, T. Fauzi, N. Talib, T. Al-Shabibi, B. Banihammad","doi":"10.2118/204700-ms","DOIUrl":"https://doi.org/10.2118/204700-ms","url":null,"abstract":"\u0000 The objective of this work was to quantify the in-situ stress contrast between the reservoir and the surrounding dense carbonate layers above and below for accurate hydraulic fracturing propagation modelling and precise fracture containment prediction. The goal was to design an optimum reservoir stimulation treatment in a Lower Cretaceous tight oil reservoir without fracturing the lower dense zone and communicating the high-permeability reservoir below. This case study came from Abu Dhabi onshore where a vertical pilot hole was drilled to perform in-situ stress testing to design a horizontal multi-stage hydraulic fractured well in a 35-ft thick reservoir.\u0000 The in-situ stress tests were obtained using a wireline straddle packer microfrac tool able to measure formation breakdown and fracture closure pressures in multiple zones across the dense and reservoir layers. Standard dual-packer micro-injection tests were conducted to measure stresses in reservoir layers while single-packer sleeve-frac tests were done to breakdown high-stress dense layers. The pressure versus time was monitored in real-time to make prompt geoscience decisions during the acquisition of the data. The formation breakdown and fracture closure pressures were utilized to calibrated minimum and maximum lateral tectonic strains for accurate in-situ stress profile. Then, the calibrated stress profile was used to simulate fracture propagation and containment for the subsequent reservoir stimulation design.\u0000 A total 17 microfrac stress tests were completed in 13 testing points across the vertical pilot, 12 with dual-packer injection and 5 with single-packer sleeve fracturing inflation. The fracture closure results showed stronger stress contrast towards the lower dense zone (900 psi) in comparison with the upper dense zone (600 psi). These measurements enabled the oilfield operating company to place the lateral well in a lower section of the tight reservoir without the risk of fracturing out-of-zone.\u0000 The novelty of this in-situ stress testing consisted of single packer inflations (sleeve frac) in an 8½-in hole in order to achieve higher differential pressures (7,000 psi) to breakdown the dense zones. The single packer breakdown permitted fracture propagation and reliable closure measurements with dual-packer injection at a lower differential reopening pressure (4,500 psi). Microfracturing the tight formation prior to fluid sampling produced clean oil samples with 80% reduction of pump out time in comparison to conventional straddle packer sampling operations. This was a breakthrough operational outcome in sampling this reservoir.","PeriodicalId":11024,"journal":{"name":"Day 4 Wed, December 01, 2021","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81938480","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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