The objectives of this project are to develop novel and more efficient materials to be used as sorbents for crude oil spills and to explore their potential for retrieval. A number of remedies have been explored for the collection of spilled crude oil, which is one of the major environmental problems. Sorbents of different forms are being used as one of these remedies. However, adsorbed oil spill and used sorbents provide a secondary source of pollution. In our laboratories, we have developed polymeric nanofibrous sorbents that showed a high affinity towards the sorption of spilled crude oil. Moreover, the water-free adsorbed oil as well as the used sorbent were completely recycled into a homogenous solution that can be added to the refinery feedstock. Polymeric materials of various origins were converted into micro- and nanofibrous sorbents using a novel nanotechnology approach. Those fibers are characterized by their high surface area, and interconnected porosities. The homogeneity of the fiber size and pore size distributions was optimized in order to maintain a high degree of reproducibility. Fibrous sorbents were therefor applied to crude oil spills that were intentionally made over a simulated sea water medium. Results showed that sorption of the crude oil spill started within seconds of contact between the fibrous sorbent and the spilled oil. The water-free adsorbed oil showed a sorption capacity of up to 217 g/g of the fibrous sorbent. Moreover, the adsorbed oil as well as the used fibrous sorbent were completely converted to a homogeneous solution that can be directly forwarded as a feedstock to the refinery, hence provide a high economic value of retrieving the spilled oil and minimizing secondary sources of pollution. The current approach is solely developed and tested in our laboratories and provides a more efficient approach to the cleanup and retrieval of crude oil spills from aqueous media. Our sorbents are competitively less expensive than those imported with a higher sorption efficiency. Moreover, our approach provides an additional solution to spilled oil and used sorbents through their recycling and re-use in the refinery feedstock.
{"title":"Cleaning and Retrieval of Spilled Crude Oil and its Application as a Refinery Feedstock Using Highly Efficient Polymeric Nanofibrous Sorbents","authors":"M. Alnaqbi, Afra Alblooshi, Y. Greish","doi":"10.2118/193256-MS","DOIUrl":"https://doi.org/10.2118/193256-MS","url":null,"abstract":"\u0000 \u0000 \u0000 The objectives of this project are to develop novel and more efficient materials to be used as sorbents for crude oil spills and to explore their potential for retrieval.\u0000 \u0000 \u0000 \u0000 A number of remedies have been explored for the collection of spilled crude oil, which is one of the major environmental problems. Sorbents of different forms are being used as one of these remedies. However, adsorbed oil spill and used sorbents provide a secondary source of pollution. In our laboratories, we have developed polymeric nanofibrous sorbents that showed a high affinity towards the sorption of spilled crude oil. Moreover, the water-free adsorbed oil as well as the used sorbent were completely recycled into a homogenous solution that can be added to the refinery feedstock.\u0000 \u0000 \u0000 \u0000 Polymeric materials of various origins were converted into micro- and nanofibrous sorbents using a novel nanotechnology approach. Those fibers are characterized by their high surface area, and interconnected porosities. The homogeneity of the fiber size and pore size distributions was optimized in order to maintain a high degree of reproducibility. Fibrous sorbents were therefor applied to crude oil spills that were intentionally made over a simulated sea water medium. Results showed that sorption of the crude oil spill started within seconds of contact between the fibrous sorbent and the spilled oil. The water-free adsorbed oil showed a sorption capacity of up to 217 g/g of the fibrous sorbent. Moreover, the adsorbed oil as well as the used fibrous sorbent were completely converted to a homogeneous solution that can be directly forwarded as a feedstock to the refinery, hence provide a high economic value of retrieving the spilled oil and minimizing secondary sources of pollution.\u0000 \u0000 \u0000 \u0000 The current approach is solely developed and tested in our laboratories and provides a more efficient approach to the cleanup and retrieval of crude oil spills from aqueous media. Our sorbents are competitively less expensive than those imported with a higher sorption efficiency. Moreover, our approach provides an additional solution to spilled oil and used sorbents through their recycling and re-use in the refinery feedstock.\u0000","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84768021","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}
F. Noordin, L. Mosse, Abdullah Albuali, Suvodip Dasgupta, I. Raina, T. Zhou
Reservoir monitoring carried out using previous-generation pulsed neutron logging tools worked well in ideal borehole conditions. However, evaluations were complicated in non-ideal borehole environments, such as gas in the borehole, which affects capture cross section, sigma, and thermal neutron porosity measurements, changing borehole fluid holdup, which confuses carbon-oxygen interpretation, and identifying hydrocarbon type using only neutron porosity when oil density and hydrogen index are very low or open hole (OH) data are unavailable. A new-generation pulsed neutron logging tool has been introduced that benefits from a high output neutron generator, two LaBr3 detectors, one yttrium aluminum perovskite (YAP) detector, one neutron source monitor, and an improved acquisition sequence. It provides self-compensated measurements of sigma and thermal neutron porosity, along with full capture and inelastic spectroscopy, including total organic carbon (TOC) and carbon-oxygen ratios. This tool also measures a new formation property called the fast neutron cross section (FNXS), which provides a gas saturation estimate independent of conventional methods. All measurements are recorded in the same logging pass, thus reducing overall logging operation time. Pulsed neutron measurements were acquired in lateral wells using the new generation tool in the A field, onshore Abu Dhabi. Through lateral sections with changing oil, water, and gas holdups in the borehole, and in changing completion environments, robust sigma and neutron porosity measurements were acquired with the help of the automatic self-compensation algorithm. Neutron porosity helped quantify gas saturations where the OH data are available and of good quality. However, in zones where it is not possible to use the neutron porosity by itself (for example, in zones with missing or uncertain OH results), the FNXS measurement provided an independent estimate of gas presence and saturation. FNXS of brine (7.5 1/m), calcite (7.5), and oil (6.0 to 7.0), are similar and strongly contrast with the FNXS of gas (1.5 to 2.5). Thus, the measurement is insensitive to porosity by itself but highly sensitive to gas presence. A crossplot of thermal neutron porosity (TPHI) and FNXS provides a robust estimate of gas saturation in wells where OH results are uncertain or not available. This paper presents, through multiple examples, a first comprehensive look at the various challenges faced while logging lateral wells in a light oil environment and showcases how a combination of self-compensated measurements coupled with the new measurement of FNXS can make data interpretation more robust in complex borehole and completion environments.
{"title":"Step Change in Reservoir Monitoring in Complex Borehole Environments","authors":"F. Noordin, L. Mosse, Abdullah Albuali, Suvodip Dasgupta, I. Raina, T. Zhou","doi":"10.2118/192648-MS","DOIUrl":"https://doi.org/10.2118/192648-MS","url":null,"abstract":"\u0000 Reservoir monitoring carried out using previous-generation pulsed neutron logging tools worked well in ideal borehole conditions. However, evaluations were complicated in non-ideal borehole environments, such as gas in the borehole, which affects capture cross section, sigma, and thermal neutron porosity measurements, changing borehole fluid holdup, which confuses carbon-oxygen interpretation, and identifying hydrocarbon type using only neutron porosity when oil density and hydrogen index are very low or open hole (OH) data are unavailable.\u0000 A new-generation pulsed neutron logging tool has been introduced that benefits from a high output neutron generator, two LaBr3 detectors, one yttrium aluminum perovskite (YAP) detector, one neutron source monitor, and an improved acquisition sequence. It provides self-compensated measurements of sigma and thermal neutron porosity, along with full capture and inelastic spectroscopy, including total organic carbon (TOC) and carbon-oxygen ratios. This tool also measures a new formation property called the fast neutron cross section (FNXS), which provides a gas saturation estimate independent of conventional methods. All measurements are recorded in the same logging pass, thus reducing overall logging operation time.\u0000 Pulsed neutron measurements were acquired in lateral wells using the new generation tool in the A field, onshore Abu Dhabi. Through lateral sections with changing oil, water, and gas holdups in the borehole, and in changing completion environments, robust sigma and neutron porosity measurements were acquired with the help of the automatic self-compensation algorithm. Neutron porosity helped quantify gas saturations where the OH data are available and of good quality. However, in zones where it is not possible to use the neutron porosity by itself (for example, in zones with missing or uncertain OH results), the FNXS measurement provided an independent estimate of gas presence and saturation. FNXS of brine (7.5 1/m), calcite (7.5), and oil (6.0 to 7.0), are similar and strongly contrast with the FNXS of gas (1.5 to 2.5). Thus, the measurement is insensitive to porosity by itself but highly sensitive to gas presence. A crossplot of thermal neutron porosity (TPHI) and FNXS provides a robust estimate of gas saturation in wells where OH results are uncertain or not available.\u0000 This paper presents, through multiple examples, a first comprehensive look at the various challenges faced while logging lateral wells in a light oil environment and showcases how a combination of self-compensated measurements coupled with the new measurement of FNXS can make data interpretation more robust in complex borehole and completion environments.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81499013","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}
Hydraulic fracturing has evolved at a rapid pace over the last decade, Permian Basin being the center of all the evolution. In 2011, vertical wells were completed on a day-time operations model with 10 to 13 different frac stages with different volumes of proppants and complex fluid systems. The same engineering & consulting teams in 2013 were challenged with horizontal wells to be completed using cost effective slickwater designs with high volumes of water and proppant on a 24-hour operations model. Finally, in 2018, Operational excellence with engineering accuracy has been possible due to lot of tweaking. This paper would present the successful completion strategies that have been put together from the lessons learnt over 5000 frac stages done in the field. The difference between success and failure is often times determined in the way we rig-up even before the well head is open. Customer’s desire in total clean fluid pumped per stage or total proppant placement efficiency or both. Moving between fluids, slick water systems to linear and cross link fluids is very important to our success. Higher rates with slick water frac-jobs giving us high penetration using low viscosity fluids and the "sand banks" formed in the lower parts of the zone are swept using linear or dirty gel sweeps. Towards the end when there is no width available for the fractures we switch to cross-link fluids that provide more near wellbore conductivity with less penetration and, using high viscosity fluids and resulted in a perfect support system. Application of both the frac-theories on real-time changes was possible with the Men & Machine integration in the Permian basin. The key characteristics of the Hybrid frac-jobs are:Usage of 100 or 40/70 mesh sand helped in fluid loss that also acts as a micro fracture proppant;Spear-heading with 2 batches of acids helped in breaking the zone and cleaning the perforationsDual Slickwater Pads with a pro-slug helped to get the penetration that we needed and also helps in determining the concentrations of the proppant behind the pad. This reduces our chances of screening out.Transition from 30/50 proppant to 20/40 proppant concentration based on the way the zone reacts to the proppant.Use of linear gel spacers allowed for higher sand concentrations in the Slickwater section of the frac-job. This provided us the width necessary.Tail ending with a 20/40 proppant using cross-linked fluids gave us a higher near wellbore conductivity This paper gives a complete outlook to field application of Hydraulic fracturing and the special use of diverters from the lessons learnt in the Permian basin.
{"title":"Permian Basin’s Evolution of Hydraulic Fracturing Techniques Over the Last Decade: Vertical to Horizontal Wells","authors":"S. Uddin, J. Cox, N. Uddin, Raheel Uddin","doi":"10.2118/193102-MS","DOIUrl":"https://doi.org/10.2118/193102-MS","url":null,"abstract":"\u0000 Hydraulic fracturing has evolved at a rapid pace over the last decade, Permian Basin being the center of all the evolution. In 2011, vertical wells were completed on a day-time operations model with 10 to 13 different frac stages with different volumes of proppants and complex fluid systems. The same engineering & consulting teams in 2013 were challenged with horizontal wells to be completed using cost effective slickwater designs with high volumes of water and proppant on a 24-hour operations model. Finally, in 2018, Operational excellence with engineering accuracy has been possible due to lot of tweaking. This paper would present the successful completion strategies that have been put together from the lessons learnt over 5000 frac stages done in the field.\u0000 The difference between success and failure is often times determined in the way we rig-up even before the well head is open. Customer’s desire in total clean fluid pumped per stage or total proppant placement efficiency or both. Moving between fluids, slick water systems to linear and cross link fluids is very important to our success. Higher rates with slick water frac-jobs giving us high penetration using low viscosity fluids and the \"sand banks\" formed in the lower parts of the zone are swept using linear or dirty gel sweeps. Towards the end when there is no width available for the fractures we switch to cross-link fluids that provide more near wellbore conductivity with less penetration and, using high viscosity fluids and resulted in a perfect support system. Application of both the frac-theories on real-time changes was possible with the Men & Machine integration in the Permian basin.\u0000 The key characteristics of the Hybrid frac-jobs are:Usage of 100 or 40/70 mesh sand helped in fluid loss that also acts as a micro fracture proppant;Spear-heading with 2 batches of acids helped in breaking the zone and cleaning the perforationsDual Slickwater Pads with a pro-slug helped to get the penetration that we needed and also helps in determining the concentrations of the proppant behind the pad. This reduces our chances of screening out.Transition from 30/50 proppant to 20/40 proppant concentration based on the way the zone reacts to the proppant.Use of linear gel spacers allowed for higher sand concentrations in the Slickwater section of the frac-job. This provided us the width necessary.Tail ending with a 20/40 proppant using cross-linked fluids gave us a higher near wellbore conductivity\u0000 This paper gives a complete outlook to field application of Hydraulic fracturing and the special use of diverters from the lessons learnt in the Permian basin.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79797263","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}
Predictive maintenance has become a major focus for the largest industrial companies because of the value it derives, including reduced downtime, improved efficiency, reduced maintenance costs, and others. Success of predictive maintenance programs is achieved when data, analytics, and subject matter expertise intersect. While data and subject matter expertise are always available, analytics talent is often lacking or facing numerous challenges which hinders the success of predictive maintenance programs. Automated model building (AMB) aims at delivering artificial intelligence to the fingertips of industrial companies and hence ensuring the success of predictive maintenance programs without the need of large data science organizations. The automated model building platform ingests the operational (sensor) and failure/fault data and automatically builds AI models to predict the remaining useful life for the asset. The patented technology behind the platform drives feature engineering and model selection which allows customers to automatically create numerous new variables from the sensor data and tests thousands of different models. The platform will then select the optimal set of variables and the model that will achieve the best performance. The entire process can be performed in a matter of few minutes without the need to know the details of all AI models. The platform also gives details on the selected models, which aids with interpretability. This paper will discuss why automated model building and artificial intelligence are needed to deliver effective, scalable predictive maintenance to the oil and gas industry, as well as specific use cases in which AI-powered automated model building has been applied.
{"title":"Applying Automated Model Building to Predictive Maintenance in Oil and Gas","authors":"P. Herve, K. Moore, M. Rosner","doi":"10.2118/192998-MS","DOIUrl":"https://doi.org/10.2118/192998-MS","url":null,"abstract":"\u0000 Predictive maintenance has become a major focus for the largest industrial companies because of the value it derives, including reduced downtime, improved efficiency, reduced maintenance costs, and others. Success of predictive maintenance programs is achieved when data, analytics, and subject matter expertise intersect. While data and subject matter expertise are always available, analytics talent is often lacking or facing numerous challenges which hinders the success of predictive maintenance programs.\u0000 Automated model building (AMB) aims at delivering artificial intelligence to the fingertips of industrial companies and hence ensuring the success of predictive maintenance programs without the need of large data science organizations.\u0000 The automated model building platform ingests the operational (sensor) and failure/fault data and automatically builds AI models to predict the remaining useful life for the asset. The patented technology behind the platform drives feature engineering and model selection which allows customers to automatically create numerous new variables from the sensor data and tests thousands of different models. The platform will then select the optimal set of variables and the model that will achieve the best performance.\u0000 The entire process can be performed in a matter of few minutes without the need to know the details of all AI models. The platform also gives details on the selected models, which aids with interpretability.\u0000 This paper will discuss why automated model building and artificial intelligence are needed to deliver effective, scalable predictive maintenance to the oil and gas industry, as well as specific use cases in which AI-powered automated model building has been applied.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"43 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77798953","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. Mahmoud, S. Elkatatny, S. AbdulmalekAhmed, M. Mahmoud
The hydrated products of Portland cement drastically change after exposure to high-temperatures, compromising the cement physical properties, especially, its compressive and tensile strengths, this phenomenon is known as strength retrogression. Previous studies showed that the use of silica flour (SF) enhances Class G oil wells cement (OWC) resistance to the strength retrogression due to the formation of long silica chains. In this work, the influence of adding modified montmorillonite nanoclay (NC) particles, which are nanoparticles of layered mineral silicates, on Class G cement strength retrogression resistance under the high-temperature condition of 300°C was evaluated. Six cement slurries were considered in this study, the base sample which has no silica or nanoclay particles, one sample contains 35% BWOC of SF particles only, and 4 samples incorporating 1.0, 2.0, 3.0, and 4.0% BWOC of NC and 35% BWOC of SF were prepared and tested under conditions of low (38°C) and high (300°C) temperature after 7 days of curing. The 300°C was selected to represent one thermal cycle condition when steam is injected into the oil well to increase the oil production for the purpose of enhanced oil recovery (EOR). After preparation, the samples were poured into different molds with specific dimensions based on the targeted test, then cured at the low-temperature condition of 38°C using a water bath, the samples were cured for 7 days. Some of the samples cured at the low temperature for the whole period while others removed in the last three days and cured at a high temperature of 300°C to mimic one steam injection cycle condition. In order to evaluate the effect of the NC particles on mitigating the cement strength loss at high-temperature, the unconfined compressive strength (UCS) and tensile strength tests were performed. The change in the permeability of the samples as a function of NC content and temperature were evaluated. The percentage loss in the water absorbed by NC particles after exposing the cement samples to the high-temperature condition (300°C) was measured. The results revealed that the use of NC (up to 3.0% BWOC) can prevent the cement deterioration under extremely high-temperature conditions of 300°C. This is attributed to two facts, first of all, the NC particles reduced the initial permeability of the samples by filling the nanoscale porous these expected to dominate the control samples (i.e. sample with 0% nanoclay), secondly acceleration of the hydration reaction which results in formation of more stable forms of calcium silicate hydrates (CSH) which leads to enhancement in the cement matrix resistance to the expected forces. At high-temperature environment, the original permeability of the NC-based cement matrix increased mainly due to evaporation of the water absorbed by NC particles when their concentration is maintained below 3.0% BWOC, the use of NC content beyond that concentration (i.e. >3.0%) severely damaged the cement matri
{"title":"Nanoclay Content Influence on Cement Strength for Oil Wells Subjected to Cyclic Steam Injection and High-Temperature Conditions","authors":"A. Mahmoud, S. Elkatatny, S. AbdulmalekAhmed, M. Mahmoud","doi":"10.2118/193059-MS","DOIUrl":"https://doi.org/10.2118/193059-MS","url":null,"abstract":"\u0000 The hydrated products of Portland cement drastically change after exposure to high-temperatures, compromising the cement physical properties, especially, its compressive and tensile strengths, this phenomenon is known as strength retrogression.\u0000 Previous studies showed that the use of silica flour (SF) enhances Class G oil wells cement (OWC) resistance to the strength retrogression due to the formation of long silica chains. In this work, the influence of adding modified montmorillonite nanoclay (NC) particles, which are nanoparticles of layered mineral silicates, on Class G cement strength retrogression resistance under the high-temperature condition of 300°C was evaluated.\u0000 Six cement slurries were considered in this study, the base sample which has no silica or nanoclay particles, one sample contains 35% BWOC of SF particles only, and 4 samples incorporating 1.0, 2.0, 3.0, and 4.0% BWOC of NC and 35% BWOC of SF were prepared and tested under conditions of low (38°C) and high (300°C) temperature after 7 days of curing. The 300°C was selected to represent one thermal cycle condition when steam is injected into the oil well to increase the oil production for the purpose of enhanced oil recovery (EOR). After preparation, the samples were poured into different molds with specific dimensions based on the targeted test, then cured at the low-temperature condition of 38°C using a water bath, the samples were cured for 7 days. Some of the samples cured at the low temperature for the whole period while others removed in the last three days and cured at a high temperature of 300°C to mimic one steam injection cycle condition.\u0000 In order to evaluate the effect of the NC particles on mitigating the cement strength loss at high-temperature, the unconfined compressive strength (UCS) and tensile strength tests were performed. The change in the permeability of the samples as a function of NC content and temperature were evaluated. The percentage loss in the water absorbed by NC particles after exposing the cement samples to the high-temperature condition (300°C) was measured.\u0000 The results revealed that the use of NC (up to 3.0% BWOC) can prevent the cement deterioration under extremely high-temperature conditions of 300°C. This is attributed to two facts, first of all, the NC particles reduced the initial permeability of the samples by filling the nanoscale porous these expected to dominate the control samples (i.e. sample with 0% nanoclay), secondly acceleration of the hydration reaction which results in formation of more stable forms of calcium silicate hydrates (CSH) which leads to enhancement in the cement matrix resistance to the expected forces. At high-temperature environment, the original permeability of the NC-based cement matrix increased mainly due to evaporation of the water absorbed by NC particles when their concentration is maintained below 3.0% BWOC, the use of NC content beyond that concentration (i.e. >3.0%) severely damaged the cement matri","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88894495","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}
T. Morrow, Tariq Al-Daghar, A. Troshko, Caroline Schell, M. W. Keller, S. Shirazi, K. Roberts
The long-term development plan for a giant oil field offshore Abu Dhabi calls for new extended reach wells drilled from artificial islands. The existing wells in this field have historically suffered from inorganic sulfate-based scale deposition in the production tubing which is mitigated by periodic scale inhibition squeeze treatments. The new extended reach wells will have more sophisticated lower completions, including limited-entry liners (LELs) and inflow control devices (ICDs) with external debris barriers. It is currently planned to mitigate inorganic scale in these wells with periodic coiled tubing or bullhead scale inhibition squeeze treatments, which are anticipated to be more challenging and costly due to the extended reach. It is unknown as to whether these types of completion equipment are susceptible to scale deposition or how much scale deposition can be tolerated before well productivity is impacted. Knowledge of the rate of scale buildup on ICDs and LELs versus the volume of water produced through the devices is an important factor for choosing the optimum frequency for scale inhibition squeeze treatments to mitigate scale in these completions while keeping operational costs down. A two-phase laboratory study is currently underway to assess the susceptibility of ICDs to scale deposition. The first phase of the study will focus on the potential for strontium sulfate scale deposition on the debris barrier upstream of the ICD. This paper reports the experimental design and results of laboratory scale deposition experiments on a series of debris barrier test coupons with the goal of estimating the rate of scale buildup on the full-size ICD debris barriers, and the volume of scaling brine that can be produced through the ICD debris barrier (in the absence of any scale inhibitor chemical) without risking significant plugging.
{"title":"Measurements of the Inorganic Scale Buildup Rate on Downhole Completion Equipment – Debris Barrier Screens","authors":"T. Morrow, Tariq Al-Daghar, A. Troshko, Caroline Schell, M. W. Keller, S. Shirazi, K. Roberts","doi":"10.2118/193311-MS","DOIUrl":"https://doi.org/10.2118/193311-MS","url":null,"abstract":"\u0000 The long-term development plan for a giant oil field offshore Abu Dhabi calls for new extended reach wells drilled from artificial islands. The existing wells in this field have historically suffered from inorganic sulfate-based scale deposition in the production tubing which is mitigated by periodic scale inhibition squeeze treatments. The new extended reach wells will have more sophisticated lower completions, including limited-entry liners (LELs) and inflow control devices (ICDs) with external debris barriers. It is currently planned to mitigate inorganic scale in these wells with periodic coiled tubing or bullhead scale inhibition squeeze treatments, which are anticipated to be more challenging and costly due to the extended reach. It is unknown as to whether these types of completion equipment are susceptible to scale deposition or how much scale deposition can be tolerated before well productivity is impacted.\u0000 Knowledge of the rate of scale buildup on ICDs and LELs versus the volume of water produced through the devices is an important factor for choosing the optimum frequency for scale inhibition squeeze treatments to mitigate scale in these completions while keeping operational costs down. A two-phase laboratory study is currently underway to assess the susceptibility of ICDs to scale deposition. The first phase of the study will focus on the potential for strontium sulfate scale deposition on the debris barrier upstream of the ICD.\u0000 This paper reports the experimental design and results of laboratory scale deposition experiments on a series of debris barrier test coupons with the goal of estimating the rate of scale buildup on the full-size ICD debris barriers, and the volume of scaling brine that can be produced through the ICD debris barrier (in the absence of any scale inhibitor chemical) without risking significant plugging.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"161 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72784839","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}
Despite numerous studies in the subject matter, industry has yet to resolve casing failure issues. A more interdisciplinary approach is taken in this study integrating seventy-eight land based wells using a data - driven approach to predict the reasons behind casing failure. This study uses a statistical software in collaboration with Python Scikit-learn implementation to apply different Data Mining and Machine Learning algorithms on twenty-four different features on the twenty failed casing data sets. Descriptive analytics manifested in visual 8representations included Normal Distribution Charts and Heat Map. Principal component Analysis (PCA) was used for dimensionality reduction. Supervised and unsupervised approaches were selected respectively based on the response. The algorithms used in this study included Support Vector Machine (SVM), Boot strap, Random Forest, Naïve Bayes, XG Boost, and K-Means Clustering. Nine models were then compared against each other to determine the winner. Features contributing to casing failure were identified based on best algorithm performance.
{"title":"Failure Predictive Analytics Using Data Mining: How to Predict Unforeseen Casing Failures?","authors":"C. Noshi, Samuel F. Noynaert, J. Schubert","doi":"10.2118/193194-MS","DOIUrl":"https://doi.org/10.2118/193194-MS","url":null,"abstract":"\u0000 Despite numerous studies in the subject matter, industry has yet to resolve casing failure issues. A more interdisciplinary approach is taken in this study integrating seventy-eight land based wells using a data - driven approach to predict the reasons behind casing failure. This study uses a statistical software in collaboration with Python Scikit-learn implementation to apply different Data Mining and Machine Learning algorithms on twenty-four different features on the twenty failed casing data sets. Descriptive analytics manifested in visual 8representations included Normal Distribution Charts and Heat Map. Principal component Analysis (PCA) was used for dimensionality reduction. Supervised and unsupervised approaches were selected respectively based on the response. The algorithms used in this study included Support Vector Machine (SVM), Boot strap, Random Forest, Naïve Bayes, XG Boost, and K-Means Clustering. Nine models were then compared against each other to determine the winner. Features contributing to casing failure were identified based on best algorithm performance.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74618907","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}
Benzene, Toluene, Ethylbenzene and Xylene (BTEX) present in feed gases to Sulfur Recovery Units (SRU) cause frequent catalyst deactivation. BTEX can be oxidized at the recommended temperatures above 1050°C. High temperatures are achieved through feed preheating and co-firing acid gas with fuel gas. However, temperatures above 1050°C is not required when BTEX concentration is low. A multi-objective optimization approach is deployed to minimize feed preheating temperature and fuel gas co-firing, while maintaining high BTEX destruction. A well validated model for Claus furnace from previous studies was used for furnace simulations. Claus furnace was modelled using Chemkin Pro, while catalytic section (including condensers, re-heaters and incinerator) was modelled using Aspen Hysys (Sulsim). MATLAB was used as a platform to link Chemkin Pro with Aspen Hysys. Optimization was performed in MATLAB using genetic algorithm. The objectives of optimization were to 1) Maximize sulfur recovery, 2) Minimize fuel gas consumption to furnace, 3) Minimize air and acid gas preheating temperature. As a constraint, total BTEX at waste heat boiler outlet (WHB) was maintained below 1ppm. The optimization range for fuel gas flow rate was from 29 to 2034 nm3/hr, air temperature from 180 to 360°C and for acid gas temperature, 180 to 230°C was considered. The feed properties and physical dimensions of SRU were obtained from an industrial SRU plant. Results show that furnace temperature of 1028°C needs to be maintained for maintaining BTEX destruction for the given feed condition examined. Thus, fuel gas co-firing can be reduced from base case value of 1773 nm3/hr to 29 nm3/hr, while air preheating temperature can also reduce from 325°C to 223°C. This can assist in reducing operational costs in sulfur recovery units considerably. The present work predicts the ideal conditions for BTEX destruction in SRUs based on inlet feed conditions. This approach can be used to seek favorable means of optimizing Sulfur recovery, decreasing fuel gas consumption in sulfur recovery units to reduce operating cost.
{"title":"Multi-Objective Optimization to Predict Minimum Temperature for Efficient BTEX Destruction to Minimize Fuel Gas Consumption in Sulfur Recovery Units","authors":"Ramees K. Rahman, S. Ibrahim, A. Raj","doi":"10.2118/192714-MS","DOIUrl":"https://doi.org/10.2118/192714-MS","url":null,"abstract":"\u0000 Benzene, Toluene, Ethylbenzene and Xylene (BTEX) present in feed gases to Sulfur Recovery Units (SRU) cause frequent catalyst deactivation. BTEX can be oxidized at the recommended temperatures above 1050°C. High temperatures are achieved through feed preheating and co-firing acid gas with fuel gas. However, temperatures above 1050°C is not required when BTEX concentration is low. A multi-objective optimization approach is deployed to minimize feed preheating temperature and fuel gas co-firing, while maintaining high BTEX destruction. A well validated model for Claus furnace from previous studies was used for furnace simulations. Claus furnace was modelled using Chemkin Pro, while catalytic section (including condensers, re-heaters and incinerator) was modelled using Aspen Hysys (Sulsim). MATLAB was used as a platform to link Chemkin Pro with Aspen Hysys. Optimization was performed in MATLAB using genetic algorithm. The objectives of optimization were to 1) Maximize sulfur recovery, 2) Minimize fuel gas consumption to furnace, 3) Minimize air and acid gas preheating temperature. As a constraint, total BTEX at waste heat boiler outlet (WHB) was maintained below 1ppm. The optimization range for fuel gas flow rate was from 29 to 2034 nm3/hr, air temperature from 180 to 360°C and for acid gas temperature, 180 to 230°C was considered. The feed properties and physical dimensions of SRU were obtained from an industrial SRU plant. Results show that furnace temperature of 1028°C needs to be maintained for maintaining BTEX destruction for the given feed condition examined. Thus, fuel gas co-firing can be reduced from base case value of 1773 nm3/hr to 29 nm3/hr, while air preheating temperature can also reduce from 325°C to 223°C. This can assist in reducing operational costs in sulfur recovery units considerably. The present work predicts the ideal conditions for BTEX destruction in SRUs based on inlet feed conditions. This approach can be used to seek favorable means of optimizing Sulfur recovery, decreasing fuel gas consumption in sulfur recovery units to reduce operating cost.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"213 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79506267","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}
Rock mechanical properties is essential for several geomechanical applications such as wellbore stability analysis, hydraulic fracturing design, and sand production management. These are often reliably determined from laboratory tests by using cores extracted from wells under simulated reservoir conditions. Unfortunately, most wells have limited core data. On the other hand, wells typically have log data, which can be used to extend the knowledge of core-based mechanical properties to the entire field. Core to log integration of rock mechanical properties and its interpretation is limited by our current understanding of rock physics. The gap is clearly evident where approximations such as empirical relationship between dynamic and static mechanical properties are used for rock failure estimation. This paper presents a hybrid framework that combines advances in digital rock physics (DRP) and machine learning (ML) to predict rock mechanical propertiy (e.g., Young's modulus) from rock mineralogy and texture to improve the accuracy of mechanical properties determined from log data. In this study, mineralogy, density, and porosity data are obtained from routine core analysis and rock mechanical property from triaxial compression tests. In our methodology, we utilized DRP models which were calibrated against core data and then generate rock mechanical property, for intervals for which triaxial measurements were not available. Mineralogy and texture data are used to create DRP models by numerically simulating rock-forming geological process including sedimentation, compaction, and cementation. Rock mechanical properties derived from DRP are used to enhance the set of training data for the ML algorithm to establish a correlation between rock mineralogy, texture, and mechanical property and construct the ML-based rock mechanical property model. The ML model generates Young's modulus predictions and are compared with the laboratory measurements. The predicted Young's modulus of rock models from the combined approach has a good agreement with the laboratory measurements. Two quantitative measures for estimation accuracy are calculated and examined including the correlation coefficient and the mean absolute percentage error. Cross-correlation plots between the Young's modulus predicted from the ML model and experimental results show high correlation coefficients and small error. The results of the study show that DRP model can be used to feed the ML model with reliable data so that the prediction accuracy can be improved. The results of this work will provide an avenue of learning from the formation lithology and using the knowledge to predict rock mechanical properties.
{"title":"Digital Rock Physics Combined with Machine Learning for Rock Mechanical Properties Characterization","authors":"Bilal Saad, Ardiansyah Negara, S. Ali","doi":"10.2118/193269-MS","DOIUrl":"https://doi.org/10.2118/193269-MS","url":null,"abstract":"\u0000 Rock mechanical properties is essential for several geomechanical applications such as wellbore stability analysis, hydraulic fracturing design, and sand production management. These are often reliably determined from laboratory tests by using cores extracted from wells under simulated reservoir conditions. Unfortunately, most wells have limited core data. On the other hand, wells typically have log data, which can be used to extend the knowledge of core-based mechanical properties to the entire field. Core to log integration of rock mechanical properties and its interpretation is limited by our current understanding of rock physics. The gap is clearly evident where approximations such as empirical relationship between dynamic and static mechanical properties are used for rock failure estimation. This paper presents a hybrid framework that combines advances in digital rock physics (DRP) and machine learning (ML) to predict rock mechanical propertiy (e.g., Young's modulus) from rock mineralogy and texture to improve the accuracy of mechanical properties determined from log data.\u0000 In this study, mineralogy, density, and porosity data are obtained from routine core analysis and rock mechanical property from triaxial compression tests. In our methodology, we utilized DRP models which were calibrated against core data and then generate rock mechanical property, for intervals for which triaxial measurements were not available. Mineralogy and texture data are used to create DRP models by numerically simulating rock-forming geological process including sedimentation, compaction, and cementation. Rock mechanical properties derived from DRP are used to enhance the set of training data for the ML algorithm to establish a correlation between rock mineralogy, texture, and mechanical property and construct the ML-based rock mechanical property model. The ML model generates Young's modulus predictions and are compared with the laboratory measurements.\u0000 The predicted Young's modulus of rock models from the combined approach has a good agreement with the laboratory measurements. Two quantitative measures for estimation accuracy are calculated and examined including the correlation coefficient and the mean absolute percentage error. Cross-correlation plots between the Young's modulus predicted from the ML model and experimental results show high correlation coefficients and small error. The results of the study show that DRP model can be used to feed the ML model with reliable data so that the prediction accuracy can be improved. The results of this work will provide an avenue of learning from the formation lithology and using the knowledge to predict rock mechanical properties.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76343028","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. Khalid, Qasim Ashraf, Khurram Luqman, Ayoub Hadji Moussa, Agha Ghulam Nabi, U. Baig, Amer Mahmood
Carbonate platforms are one of the most common reservoirs on earth, and as such are one of the most frequently explored. Sulaiman fold belt in Pakistan is known to contain multiple hydrocarbon bearing carbonate formations. One such formation is the Sui Main Limestone formation. The formation when first discovered was known to contain over 9.5 Tcf of gas in Sui field, and up to 5.0 Tcf of gas in the neighboring Zin field. Over the years due to extensive field development and production, the Sui Main Limestone reservoir has been driven to depletion. Operators are now looking to explore deeper horizons in the same fields. The challenge in deeper exploration of the subject fields is now a depleted pressure of about 2.1 ppg EMW of the Sui Main Limestone formation. In addition to the low pressure, the SML formation is highly fractured in nature. These two factors resulted in massive circulation losses when an attempt to drill a well was made through the approximately 650 m width of the SML formation. To cure losses, operators resorted to heavy LCM pills, and numerous cement plugs. Losses in the hydrocarbon bearing SML formation also led to well control and stuck pipe events on multiple occasions. Successful drilling through the whole width of SML formation would sometimes take up to almost 3 months. Drilling time and lost circulation materials thus generated excessive well costs. The operator sought a solution which would eliminate circulation losses in the SML formation, and cut down drilling time substantially. An underbalanced system was first considered for achieving these objectives but as the SML formation bore sour gas and excessive equipment would be required for a safe underbalanced operation, the option was ruled out. A nearbalanced nitrified foam system was thus designed to be able to drill the SML formation delivering the same benefits of an underbalanced operation without its perils. By applying a nearbalanced nitrified drilling technique, operators in the subject fields were able to cut down the drilling time to about 3-5 days, achieve a substantial increase in drilling performance, and practically reduce the NPT to 0. This paper studies the planning & design of a nearbalanced nitrified foam system for two different wells with hole sections of size 17", and 8-1/2". The paper also discusses the equipment selection, the wellsite execution, and the results achieved by applying nearbalanced nitrified foam drilling in the subject fields.
{"title":"Nearbalanced Nitrified Foam Drilling: A New Frontier for the Drilling of Depleted & Fractured Carbonates - A Study on the Design, Execution, and Results on Multiple Wells","authors":"A. Khalid, Qasim Ashraf, Khurram Luqman, Ayoub Hadji Moussa, Agha Ghulam Nabi, U. Baig, Amer Mahmood","doi":"10.2118/193207-MS","DOIUrl":"https://doi.org/10.2118/193207-MS","url":null,"abstract":"\u0000 Carbonate platforms are one of the most common reservoirs on earth, and as such are one of the most frequently explored.\u0000 Sulaiman fold belt in Pakistan is known to contain multiple hydrocarbon bearing carbonate formations. One such formation is the Sui Main Limestone formation. The formation when first discovered was known to contain over 9.5 Tcf of gas in Sui field, and up to 5.0 Tcf of gas in the neighboring Zin field. Over the years due to extensive field development and production, the Sui Main Limestone reservoir has been driven to depletion. Operators are now looking to explore deeper horizons in the same fields.\u0000 The challenge in deeper exploration of the subject fields is now a depleted pressure of about 2.1 ppg EMW of the Sui Main Limestone formation. In addition to the low pressure, the SML formation is highly fractured in nature. These two factors resulted in massive circulation losses when an attempt to drill a well was made through the approximately 650 m width of the SML formation. To cure losses, operators resorted to heavy LCM pills, and numerous cement plugs. Losses in the hydrocarbon bearing SML formation also led to well control and stuck pipe events on multiple occasions. Successful drilling through the whole width of SML formation would sometimes take up to almost 3 months. Drilling time and lost circulation materials thus generated excessive well costs.\u0000 The operator sought a solution which would eliminate circulation losses in the SML formation, and cut down drilling time substantially. An underbalanced system was first considered for achieving these objectives but as the SML formation bore sour gas and excessive equipment would be required for a safe underbalanced operation, the option was ruled out. A nearbalanced nitrified foam system was thus designed to be able to drill the SML formation delivering the same benefits of an underbalanced operation without its perils.\u0000 By applying a nearbalanced nitrified drilling technique, operators in the subject fields were able to cut down the drilling time to about 3-5 days, achieve a substantial increase in drilling performance, and practically reduce the NPT to 0.\u0000 This paper studies the planning & design of a nearbalanced nitrified foam system for two different wells with hole sections of size 17\", and 8-1/2\". The paper also discusses the equipment selection, the wellsite execution, and the results achieved by applying nearbalanced nitrified foam drilling in the subject fields.","PeriodicalId":11014,"journal":{"name":"Day 1 Mon, November 12, 2018","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85385858","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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