Polymer gel treatments have been widely used by the industry to improve sweep conformance and enhance recovery from highly fractured reservoirs. The success of these treatments depends on several factors that include various reservoir properties and gel design parameters. This paper presents a pragmatic approach to optimize the design of polymer gel treatments to improve oil recovery in naturally fractured reservoirs using neuro-simulation based models. A full spectrum of fractured reservoir properties and polymer gel treatment design parameters was used to generate base simulation models. Production rate, oil recovery and water cut trends were used as key performance indicators to monitor sweep conformance and evaluate polymer gel design effectiveness. These simulation models were used to construct, train and validate the neural network. The network topology was effectively designed to achieve a good match with the reservoir simulation models. A given set of reservoir properties including porosity, permeability, net pay thickness, water saturation, polymer gel concentration and injection rate can be optimized using the neural-based model to acquire the desired production rate. Furthermore, results show that the injection rate and cross-linking agent concentration are the most sensitive parameters affecting the production performance. The neural model can be used as an effective screening tool for selecting and designing polymer gel projects as it covers a wide range of field parameters. This work capitalizes on the ability of artificial expert systems in generating tractable, robust and computationally efficient solutions for complex reservoir models. In particular, this paper presents proxy models that are uniquely developed for the first time to optimize oil recovery in naturally fractured reservoirs using polymer gel conformance treatments.
{"title":"Assisted Design of Polymer-Gel Floods in Naturally Fractured Reservoirs Using Neuro-Simulation Based Models","authors":"Mohammed Alghazal, T. Ertekin","doi":"10.2118/192602-MS","DOIUrl":"https://doi.org/10.2118/192602-MS","url":null,"abstract":"\u0000 Polymer gel treatments have been widely used by the industry to improve sweep conformance and enhance recovery from highly fractured reservoirs. The success of these treatments depends on several factors that include various reservoir properties and gel design parameters. This paper presents a pragmatic approach to optimize the design of polymer gel treatments to improve oil recovery in naturally fractured reservoirs using neuro-simulation based models.\u0000 A full spectrum of fractured reservoir properties and polymer gel treatment design parameters was used to generate base simulation models. Production rate, oil recovery and water cut trends were used as key performance indicators to monitor sweep conformance and evaluate polymer gel design effectiveness. These simulation models were used to construct, train and validate the neural network. The network topology was effectively designed to achieve a good match with the reservoir simulation models.\u0000 A given set of reservoir properties including porosity, permeability, net pay thickness, water saturation, polymer gel concentration and injection rate can be optimized using the neural-based model to acquire the desired production rate. Furthermore, results show that the injection rate and cross-linking agent concentration are the most sensitive parameters affecting the production performance. The neural model can be used as an effective screening tool for selecting and designing polymer gel projects as it covers a wide range of field parameters.\u0000 This work capitalizes on the ability of artificial expert systems in generating tractable, robust and computationally efficient solutions for complex reservoir models. In particular, this paper presents proxy models that are uniquely developed for the first time to optimize oil recovery in naturally fractured reservoirs using polymer gel conformance treatments.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"180 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80147678","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. Al-ghanem, Saleh Aljabri, M. Hameed, M. Al‐Saeed, M. Al-Otaibi
Kuwait Oil Company (KOC) owns and operates several Oil & Gas fields and Pipeline networks in Kuwait and is responsible for exploration, development, production and operation of Kuwait's Hydrocarbon assets. The oil fields in the western part of the state predominantly produces high sour gas and normally the compressed sour gas is transported to downstream refineries for treatment, wherein the Acid Gas Removal Plants extract the sulfur contents in the gas received by treating it with regenerative Amine based treating processes for removing acidic impurities such as H2S, CO2 and organic Sulphur compounds. The country has been long battling with the limitations in downstream sector such as limited handling capacity, unplanned shutdowns, and delay in their expansion projects. This created huge bottlenecks for the upstream unit of KOC which consequently resulted in operational disturbances and gas flaring beyond the company's global flaring target of < 1%. To overcome these challenges, a comprehensive study was carried out for sour gas handling in the State of Kuwait and installation of Gas Sweetening Facility (NGSF) within KOC was considered imperative. However, the process of project delivery was a great challenge due to emerging operational approaches and conflicts with expansion projects in refinery. Thus, breakthrough solutions were set out deploying appropriate core technologies. This paper discusses the challenges at length and the innovative solutions implemented which were intended to optimize the production and utilization of gas in support of energy requirements for the State.
{"title":"Cutting-Edge Solutions for Sour Gas Treatment- A Retrospective Study by Kuwait Oil Company","authors":"F. Al-ghanem, Saleh Aljabri, M. Hameed, M. Al‐Saeed, M. Al-Otaibi","doi":"10.2118/192783-MS","DOIUrl":"https://doi.org/10.2118/192783-MS","url":null,"abstract":"\u0000 Kuwait Oil Company (KOC) owns and operates several Oil & Gas fields and Pipeline networks in Kuwait and is responsible for exploration, development, production and operation of Kuwait's Hydrocarbon assets. The oil fields in the western part of the state predominantly produces high sour gas and normally the compressed sour gas is transported to downstream refineries for treatment, wherein the Acid Gas Removal Plants extract the sulfur contents in the gas received by treating it with regenerative Amine based treating processes for removing acidic impurities such as H2S, CO2 and organic Sulphur compounds.\u0000 The country has been long battling with the limitations in downstream sector such as limited handling capacity, unplanned shutdowns, and delay in their expansion projects. This created huge bottlenecks for the upstream unit of KOC which consequently resulted in operational disturbances and gas flaring beyond the company's global flaring target of < 1%.\u0000 To overcome these challenges, a comprehensive study was carried out for sour gas handling in the State of Kuwait and installation of Gas Sweetening Facility (NGSF) within KOC was considered imperative. However, the process of project delivery was a great challenge due to emerging operational approaches and conflicts with expansion projects in refinery. Thus, breakthrough solutions were set out deploying appropriate core technologies. This paper discusses the challenges at length and the innovative solutions implemented which were intended to optimize the production and utilization of gas in support of energy requirements for the State.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"531 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80179204","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}
S. Subbiah, M. Povstyanova, Shimpei Egawa, S. Kokubo, K. Yahata, Takeru Okuzawa, A. Vantala, C. Tan, G. Nasreldin, Joel W. Martin, M. Husien, Nanthakumar Rajaiah
In a recently drilled deviated well in an offshore field in UAE, severe cavings have been produced which led to difficulty in tripping out and stuck pipe events. A comprehensive study has been conducted to understand the chemical and mechanical behavior of the shales in the overburden. This paper focuses on how we approached optimization of drilling design and practices where well construction was concerned (namely casing design and mud formulation). This approach minimized mechanical and time-dependent chemical instabilities in the Fiqa, Laffan and Nahr-Umr shales. After the initial implementation of the optimized drilling practices, a complex multi-discipline study including time-dependent shale stability analysis provided recommendations for the problematic shales should they be kept open for long durations (to reach section TD, log and case). The time-dependent shale stability analysis included three major phases. The first phase was conducted based on the data for several selected existing wells. This phase resulted in obtaining so called field-based mud design criteria together with customized laboratory measurements. The second phase is to conduct a comprehensive geomechanical model to understand the mechanical behavior of the formations. In this study both 1D and 3D geomechanical models have been constructed honoring the anisotropic nature of the shales. The third phase was focused on selecting best mud system and optimizing the mud designs to prevent/minimize both mechanical and time-dependent chemical instabilities for shales layers with long exposure time. The problematic shales were penetrated at relatively high angles, requiring high mud weights and therefore leading to relatively high overbalance pressures which can cause high pore pressure increase in the shales with time. However, it is still feasible to select an optimum drilling fluid design for the desired mud system by optimizing salinity for the required high mud weights to avoid time-dependent instability. The Nahr-Umr shale, in general, was deemed to be more susceptible to mechanical and time-dependent chemical instabilities due to higher required mud weights and overbalance pressures. The Fiqa, Laffan and Nahr-Umr shale formations could be drilled using the recommended mud weights together with best mud formulations to avoid both mechanical and chemical time-dependent wellbore instability problems in the planned wells. The outcome of the study helps in keeping the shales open for longer period in highly deviated wells without any wellbore instability before casing runs. The workflow utilized for the shale stability analysis for Fiqa, Laffan and Nahr-Umr included an approach innovative for UAE to understand mechanical and chemical (osmosis-related) behavior of the problematic shales to develop recommendations for cases when the shales needed be kept open for long durations.
{"title":"Chemo-Mechanical Behavior for UAE Shales and Mud Design for Wellbore Stability","authors":"S. Subbiah, M. Povstyanova, Shimpei Egawa, S. Kokubo, K. Yahata, Takeru Okuzawa, A. Vantala, C. Tan, G. Nasreldin, Joel W. Martin, M. Husien, Nanthakumar Rajaiah","doi":"10.2118/192905-MS","DOIUrl":"https://doi.org/10.2118/192905-MS","url":null,"abstract":"\u0000 In a recently drilled deviated well in an offshore field in UAE, severe cavings have been produced which led to difficulty in tripping out and stuck pipe events. A comprehensive study has been conducted to understand the chemical and mechanical behavior of the shales in the overburden.\u0000 This paper focuses on how we approached optimization of drilling design and practices where well construction was concerned (namely casing design and mud formulation). This approach minimized mechanical and time-dependent chemical instabilities in the Fiqa, Laffan and Nahr-Umr shales. After the initial implementation of the optimized drilling practices, a complex multi-discipline study including time-dependent shale stability analysis provided recommendations for the problematic shales should they be kept open for long durations (to reach section TD, log and case).\u0000 The time-dependent shale stability analysis included three major phases. The first phase was conducted based on the data for several selected existing wells. This phase resulted in obtaining so called field-based mud design criteria together with customized laboratory measurements. The second phase is to conduct a comprehensive geomechanical model to understand the mechanical behavior of the formations. In this study both 1D and 3D geomechanical models have been constructed honoring the anisotropic nature of the shales. The third phase was focused on selecting best mud system and optimizing the mud designs to prevent/minimize both mechanical and time-dependent chemical instabilities for shales layers with long exposure time.\u0000 The problematic shales were penetrated at relatively high angles, requiring high mud weights and therefore leading to relatively high overbalance pressures which can cause high pore pressure increase in the shales with time. However, it is still feasible to select an optimum drilling fluid design for the desired mud system by optimizing salinity for the required high mud weights to avoid time-dependent instability. The Nahr-Umr shale, in general, was deemed to be more susceptible to mechanical and time-dependent chemical instabilities due to higher required mud weights and overbalance pressures.\u0000 The Fiqa, Laffan and Nahr-Umr shale formations could be drilled using the recommended mud weights together with best mud formulations to avoid both mechanical and chemical time-dependent wellbore instability problems in the planned wells. The outcome of the study helps in keeping the shales open for longer period in highly deviated wells without any wellbore instability before casing runs.\u0000 The workflow utilized for the shale stability analysis for Fiqa, Laffan and Nahr-Umr included an approach innovative for UAE to understand mechanical and chemical (osmosis-related) behavior of the problematic shales to develop recommendations for cases when the shales needed be kept open for long durations.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78115507","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}
Drilling waste generated during the drilling of wells using oil-based muds (OBMs) can often contain a high level of oily waste liquid as a result of surface mud losses, fluid displacements, rig wash down activities, and rig tank cleaning. This type of waste commonly known as "drilling slops" represents a significant volume of the overall waste generated while drilling a well and contributes to the overall environmental impact, cost of waste haul, and final disposal. In addition, the use of emulsifiers and other chemicals in OBMs leads to these liquids becoming difficult and expensive to treat efficiently with conventional separation and treatment systems. This paper sets out a new method for treatment and recycling of this type of waste for land drilling operations that achieved a 73% reduction in waste volumes generated compared to other wells drilled in the same area. The results in the paper will also demonstrate that the oil and water recovered by this system was within the recommended quality parameters for recycling in the drilling operation. This system significantly reduces the need to transport wastes for offsite treatment and disposal while reducing the overall environmental impact of the drilling operation. After analyzing the source of wastes generated during drilling at a land location in Algeria, a methodology was devised to segregate drilling wastes and avoid the co-mingling of different waste types before sending the drilling slops to the system for treatment. Lab tests were carried out to determine the optimum flocculants and dosing rates required to separate and recover the oil and water from the solids. This new method for treating and recycling these waste is an integrated chemical flocculation and dewatering system using a container fabricated from a specially engineered textile that provides confinement and drying of drill solids inside the container while allowing the liquids to permeate through the engineered textile for recycling and reuse on the rig. This system reduces the amount of liquid wastes hauled off site for treatment leaving dried solids that are easily handled and disposed of with conventional treatment methods. The use of this technology can have an important and cost effective contribution to reducing the environmental impact of land drilling operations using OBMs in Algeria and beyond.
{"title":"New Engineering Approach for Environmental Impact Mitigation in Drilling Operations","authors":"Adlane Daoudi, Salim Sator, Bellatache Samira","doi":"10.2118/192774-MS","DOIUrl":"https://doi.org/10.2118/192774-MS","url":null,"abstract":"\u0000 Drilling waste generated during the drilling of wells using oil-based muds (OBMs) can often contain a high level of oily waste liquid as a result of surface mud losses, fluid displacements, rig wash down activities, and rig tank cleaning. This type of waste commonly known as \"drilling slops\" represents a significant volume of the overall waste generated while drilling a well and contributes to the overall environmental impact, cost of waste haul, and final disposal. In addition, the use of emulsifiers and other chemicals in OBMs leads to these liquids becoming difficult and expensive to treat efficiently with conventional separation and treatment systems.\u0000 This paper sets out a new method for treatment and recycling of this type of waste for land drilling operations that achieved a 73% reduction in waste volumes generated compared to other wells drilled in the same area. The results in the paper will also demonstrate that the oil and water recovered by this system was within the recommended quality parameters for recycling in the drilling operation. This system significantly reduces the need to transport wastes for offsite treatment and disposal while reducing the overall environmental impact of the drilling operation.\u0000 After analyzing the source of wastes generated during drilling at a land location in Algeria, a methodology was devised to segregate drilling wastes and avoid the co-mingling of different waste types before sending the drilling slops to the system for treatment. Lab tests were carried out to determine the optimum flocculants and dosing rates required to separate and recover the oil and water from the solids. This new method for treating and recycling these waste is an integrated chemical flocculation and dewatering system using a container fabricated from a specially engineered textile that provides confinement and drying of drill solids inside the container while allowing the liquids to permeate through the engineered textile for recycling and reuse on the rig. This system reduces the amount of liquid wastes hauled off site for treatment leaving dried solids that are easily handled and disposed of with conventional treatment methods.\u0000 The use of this technology can have an important and cost effective contribution to reducing the environmental impact of land drilling operations using OBMs in Algeria and beyond.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 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":"73842465","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}
Hazard identification is one of the most important activities carried out in the Safety Instrumented System (SIS) safety lifecycle [1]. Proper hazard identification and analysis of its risk lays the foundation of the SIS design. The common method for a structured study for the hazard identification is Hazard and Operability Study (HAZOP) study. The concepts of HAZOP are well evolved and applied for over five decades. The basic premise for HAZOP considers plant design is mature enough and sufficient design information on the plant operation is available. HAZOP process involves breaking down of complex process into simpler sections which are termed as nodes. These individual nodes are then studied for identifying the potential hazards and operability problems. STAMP (System-Theoretic Accident Model and Processing) [2] is accident causality model based on systems theory. STPA (System Theoretic Process Analysis) is one of the STAMP based tool which is a relatively new hazard analysis technique based on an extended model of accident causation. STPA is a proactive analysis method that analyzes the potential cause of accidents during design development so that hazards can be eliminated or controlled. Conventional studies like HAZOP considers deviations or component failures as cause for what may go wrong and cause accident. STPA assumes that accident may also be caused due to unsafe interactions of the system components, none of which have failed.
危害识别是安全仪表系统(SIS)安全生命周期中最重要的活动之一。正确的危险识别和风险分析是SIS设计的基础。危害识别的结构化研究的常用方法是危害和可操作性研究(HAZOP)研究。HAZOP的概念经过了50多年的发展和应用。HAZOP的基本前提是工厂设计足够成熟，并且有足够的工厂运行设计信息。HAZOP过程将复杂的过程分解成简单的部分，这些部分被称为节点。然后对这些单独的节点进行研究，以确定潜在的危险和可操作性问题。STAMP (system - theory Accident Model and Processing，系统理论事故模型与处理)[2]是基于系统理论的事故因果关系模型。系统理论过程分析(System theoretical Process Analysis, STPA)是一种基于STAMP的工具，是一种基于事故原因扩展模型的较新的危害分析技术。STPA是一种主动分析方法，在设计开发过程中分析事故的潜在原因，从而消除或控制危险。像HAZOP这样的传统研究认为偏差或部件故障是可能出错和导致事故的原因。STPA假设事故也可能是由于系统组件的不安全交互引起的，这些组件都没有发生故障。
{"title":"Application of STAMP to Process Industry","authors":"Amit Aglave, Debopam Chaudhuri, Stephen Johnson","doi":"10.2118/192756-MS","DOIUrl":"https://doi.org/10.2118/192756-MS","url":null,"abstract":"\u0000 \u0000 \u0000 Hazard identification is one of the most important activities carried out in the Safety Instrumented System (SIS) safety lifecycle [1]. Proper hazard identification and analysis of its risk lays the foundation of the SIS design.\u0000 \u0000 \u0000 \u0000 The common method for a structured study for the hazard identification is Hazard and Operability Study (HAZOP) study. The concepts of HAZOP are well evolved and applied for over five decades. The basic premise for HAZOP considers plant design is mature enough and sufficient design information on the plant operation is available. HAZOP process involves breaking down of complex process into simpler sections which are termed as nodes. These individual nodes are then studied for identifying the potential hazards and operability problems.\u0000 STAMP (System-Theoretic Accident Model and Processing) [2] is accident causality model based on systems theory. STPA (System Theoretic Process Analysis) is one of the STAMP based tool which is a relatively new hazard analysis technique based on an extended model of accident causation. STPA is a proactive analysis method that analyzes the potential cause of accidents during design development so that hazards can be eliminated or controlled. Conventional studies like HAZOP considers deviations or component failures as cause for what may go wrong and cause accident. STPA assumes that accident may also be caused due to unsafe interactions of the system components, none of which have failed.\u0000","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81828701","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}
Rahul Shiwang, T. Chandrashekar, Anirban Banerjee, Srimanta Chakraborty, V. Telang, C. Deshpande, S. Malik
A number of exploratory wells were drilled in Eastern Offshore of India, encountering thick turbiditic sequences. The formation evaluation through conventional logging tools is a challenge in such depositional environments as the tools are unable to resolve thin beds and provides a weighted average log response over a collection of beds. In such environments, often the potential pay intervals are overlooked if comprehensive petrophysical analysis is not carried out. While the thin bed problem underestimates the reservoir potential, the orientation of measurement of the petrophysical properties further complicates the problem due to formation anisotropy. Another important characteristic of layered thin bed sand shale sequence is the acoustic anisotropy due to the transversely isotropic nature of sedimentary deposition. The multicomponent induction tool was logged in the study area, providing a tensor measurement of the horizontal (Rh) and vertical (Rv) components of resistivity. The well encountered thick turbidite sequence of laminated pay sands with very low resistivity contrast. The initial stochastic petrophysical analysis from conventional open hole log responses indicated poor reservoir quality with high water saturation. Integration of high-resolution acoustic data and VTI analysis with multicomponent induction tool shows a clear evidence of alternating shale and sand sequences in the target reservoir. A high-resolution processed acoustic porosity was incorporated to build the lithology model with multicomponent resistivity data. Integration of ResH, ResV and VTI into a Thomas-Stieber petrophysical model indicates potential hydrocarbon bearing sands at two depths which were further included to optimise the formation testing and sampling plan. During fluid sampling at the two identified depths, 54 and 157 ltrs. of fluid volume was pumped out before collecting samples by utilizing real time downhole fluid identification technologies. Optical absorbance and refractive indices were used to differentiate between miscible fluids. Clean-up from SOBM to formation oil was monitored using trends in representative channels of constantly changing absorbance spectrum. The formation testing results, therefore, were in good agreement with the identified pay intervals from the T-S model. Furthermore, Stoneley permeability analysis were carried out in the study area and calibrated with formation testing results. In the absence of imager data in the example well, formation dip was computed based on the multicomponent induction tool, which provided a close match to the OBM imagers, which struggled due to low formation resistivity, logged in adjacent wells. This paper highlights the integrated workflow of multicomponent resistivity data based Thomas Stieber petrophysical model with high resolution acoustic and formation tester results of the example well and its success in delineation of pay sand intervals.
{"title":"Integrated Approach of Reservoir Characterization Using Multi-Component Induction Tool in Thinly Laminated Pay Sands- A Case Study from Eastern Offshore India","authors":"Rahul Shiwang, T. Chandrashekar, Anirban Banerjee, Srimanta Chakraborty, V. Telang, C. Deshpande, S. Malik","doi":"10.2118/192802-MS","DOIUrl":"https://doi.org/10.2118/192802-MS","url":null,"abstract":"\u0000 A number of exploratory wells were drilled in Eastern Offshore of India, encountering thick turbiditic sequences. The formation evaluation through conventional logging tools is a challenge in such depositional environments as the tools are unable to resolve thin beds and provides a weighted average log response over a collection of beds. In such environments, often the potential pay intervals are overlooked if comprehensive petrophysical analysis is not carried out. While the thin bed problem underestimates the reservoir potential, the orientation of measurement of the petrophysical properties further complicates the problem due to formation anisotropy. Another important characteristic of layered thin bed sand shale sequence is the acoustic anisotropy due to the transversely isotropic nature of sedimentary deposition.\u0000 The multicomponent induction tool was logged in the study area, providing a tensor measurement of the horizontal (Rh) and vertical (Rv) components of resistivity. The well encountered thick turbidite sequence of laminated pay sands with very low resistivity contrast. The initial stochastic petrophysical analysis from conventional open hole log responses indicated poor reservoir quality with high water saturation. Integration of high-resolution acoustic data and VTI analysis with multicomponent induction tool shows a clear evidence of alternating shale and sand sequences in the target reservoir. A high-resolution processed acoustic porosity was incorporated to build the lithology model with multicomponent resistivity data. Integration of ResH, ResV and VTI into a Thomas-Stieber petrophysical model indicates potential hydrocarbon bearing sands at two depths which were further included to optimise the formation testing and sampling plan.\u0000 During fluid sampling at the two identified depths, 54 and 157 ltrs. of fluid volume was pumped out before collecting samples by utilizing real time downhole fluid identification technologies. Optical absorbance and refractive indices were used to differentiate between miscible fluids. Clean-up from SOBM to formation oil was monitored using trends in representative channels of constantly changing absorbance spectrum. The formation testing results, therefore, were in good agreement with the identified pay intervals from the T-S model. Furthermore, Stoneley permeability analysis were carried out in the study area and calibrated with formation testing results. In the absence of imager data in the example well, formation dip was computed based on the multicomponent induction tool, which provided a close match to the OBM imagers, which struggled due to low formation resistivity, logged in adjacent wells.\u0000 This paper highlights the integrated workflow of multicomponent resistivity data based Thomas Stieber petrophysical model with high resolution acoustic and formation tester results of the example well and its success in delineation of pay sand intervals.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"1996 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88150389","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}
Bouchra Lamik-Thonhauser, J. Schoen, C. Koller, A. Arnaout
Drilling process is fundamentally controlled and influenced by the properties of the penetrated formation. The focus of various studies is directed mainly on the optimal design of drill bit and drilling operations related to the (expected) geological situation of a safe drilling process. Out of interest the question "Is it possible to extract any lithologic information from drilling data?" also arises. The drilling process at the bit represents a complicated rock mechanical process. The drill bit acts as a rotating cutter, controlled mainly by weight on bit (WOB), bit size, number of revolutions per time (RPM), which controls speed of the cutting process. We define a "cutting force Fc" as a combination of these parameters and investigate the relationship between Fc and rate of penetration (ROP). For the correlation with lithology of penetrated formations, two methods are applied on test wells: – crossplots Fc versus ROP with discrimination for dominant lithology. – histograms of the ratio of two variables for dominant lithology. Drilling data from two basins are analyzed (in both cases wells are nearly vertical): – Vienna Basin: Dominant lithologies are sandstones (varying degree of cementation), shale/marls, limestones, and dolomites. – Williston Basin: Dominant lithologies in the analysed section are sandstone shale, dolomitic limestone, and limestone with anhydrite. Data points from the rocks with similar lithology in the crossplot are situated in a cloud - different rocks show different cloud position. Particularly between clastic (sand, silt, shale) and carbonate (limestone, dolomite) rocks, a clear separation is visible. The implementation of lines for a constant ratio Fc/ROP in the crossplot separate the different lithologies. Therefore, the statistical distribution of this ratio S = Fc/ROP for the dominant lithologies was analyzed by histograms. For the two test wells, histograms separate the different lithologies and confirm the information content. The drilling process is controlled by rock type; the analyse of drill-process data can be used for a lithological discrimination and especially for detecting changing lithology (boundaries) during drilling process. For two test wells, the discrimination could be demonstrated by the crossplot and histogram technique. The exact position of discriminator magnitudes (center of data clouds in the crossplots, peaks in the histograms) is specific for the considered field and may be controlled by more drilling parameters.
{"title":"Correlation Between Drilling Parameters and Lithology - The Hidden Geological Information of Drilling Data","authors":"Bouchra Lamik-Thonhauser, J. Schoen, C. Koller, A. Arnaout","doi":"10.2118/192916-MS","DOIUrl":"https://doi.org/10.2118/192916-MS","url":null,"abstract":"\u0000 Drilling process is fundamentally controlled and influenced by the properties of the penetrated formation. The focus of various studies is directed mainly on the optimal design of drill bit and drilling operations related to the (expected) geological situation of a safe drilling process.\u0000 Out of interest the question \"Is it possible to extract any lithologic information from drilling data?\" also arises.\u0000 The drilling process at the bit represents a complicated rock mechanical process. The drill bit acts as a rotating cutter, controlled mainly by weight on bit (WOB), bit size, number of revolutions per time (RPM), which controls speed of the cutting process. We define a \"cutting force Fc\" as a combination of these parameters and investigate the relationship between Fc and rate of penetration (ROP). For the correlation with lithology of penetrated formations, two methods are applied on test wells:\u0000 – crossplots Fc versus ROP with discrimination for dominant lithology. – histograms of the ratio of two variables for dominant lithology.\u0000 Drilling data from two basins are analyzed (in both cases wells are nearly vertical):\u0000 – Vienna Basin: Dominant lithologies are sandstones (varying degree of cementation), shale/marls, limestones, and dolomites. – Williston Basin: Dominant lithologies in the analysed section are sandstone shale, dolomitic limestone, and limestone with anhydrite.\u0000 Data points from the rocks with similar lithology in the crossplot are situated in a cloud - different rocks show different cloud position. Particularly between clastic (sand, silt, shale) and carbonate (limestone, dolomite) rocks, a clear separation is visible.\u0000 The implementation of lines for a constant ratio Fc/ROP in the crossplot separate the different lithologies. Therefore, the statistical distribution of this ratio S = Fc/ROP for the dominant lithologies was analyzed by histograms. For the two test wells, histograms separate the different lithologies and confirm the information content.\u0000 The drilling process is controlled by rock type; the analyse of drill-process data can be used for a lithological discrimination and especially for detecting changing lithology (boundaries) during drilling process. For two test wells, the discrimination could be demonstrated by the crossplot and histogram technique. The exact position of discriminator magnitudes (center of data clouds in the crossplots, peaks in the histograms) is specific for the considered field and may be controlled by more drilling parameters.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87235481","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}
Slim hole well design can reduce well construction cost through a reduction in steel, fluids, and disposal costs. In the industry, there has been a misconception that slim hole size possesses the tradeoff of slower ROP and less efficient fracture treatment. Improvements in downhole tools, drill strings, rig capability, and drilling fluid design have been implemented to improve ROP in slim holes. Completions designs were refined for slimmer holes to avoid any significant loss in stimulation effectiveness and maintain well value. Through systematic replication of learnings and designs across basins, slim hole well design has advanced. Slim hole well design (transition from 8.5" / 7.875" hole and 5.5" production casing to 6.75" hole and 5.5" / 4.5" production casing) can reduce Unconventional well cost by over 25% which reduces well unit development cost (UDC).
{"title":"Reducing Horizontal Hole Size from 8.5 to 6.75 Reduces Unconventional Well Construction Cost by 25%","authors":"Eric David Schumacker, Philip Vogelsberg","doi":"10.2118/192953-MS","DOIUrl":"https://doi.org/10.2118/192953-MS","url":null,"abstract":"\u0000 Slim hole well design can reduce well construction cost through a reduction in steel, fluids, and disposal costs. In the industry, there has been a misconception that slim hole size possesses the tradeoff of slower ROP and less efficient fracture treatment. Improvements in downhole tools, drill strings, rig capability, and drilling fluid design have been implemented to improve ROP in slim holes. Completions designs were refined for slimmer holes to avoid any significant loss in stimulation effectiveness and maintain well value. Through systematic replication of learnings and designs across basins, slim hole well design has advanced. Slim hole well design (transition from 8.5\" / 7.875\" hole and 5.5\" production casing to 6.75\" hole and 5.5\" / 4.5\" production casing) can reduce Unconventional well cost by over 25% which reduces well unit development cost (UDC).","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 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":"90713135","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}
Luca Cadei, M. Montini, Fabio Landi, F. Porcelli, V. Michetti, M. Origgi, Marco Tonegutti, S. Duranton
This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to mitigate or avoid operational issues, maximizing the asset value, while maintaining the highest safety and environmental quality. This in-house developed tool is based on big data analytics techniques such as machine and deep learning algorithms. The workflow developed allows predicting future events and the related influencing variables. This is done thanks to a powerful machine-learning algorithm specifically selected for the physical problem analyzed. The inputs come from a heterogeneous data-lake, composed by historical data, real-time series, maintenance reports, chemical analysis and operator experience. The workflow developed starts processing and enhancing this huge amount of data in order to train and validate the selected algorithm. Finally, the tool is fed with real-time data from the field, predicting potential events and prescribing possible actions to avoid problems that jeopardize the production and the integrity of the asset. The tool has demonstrated the capability to predict in advance operational upsets occurring within the entire production system avoiding issues, maximizing the field availability. The case illustrated in this paper focuses the attention on the process section of an upstream oil field. In particular, process upsets of the sweetening unit, such as H2S out of specification, are analyzed since they affect not only the field production, but also the asset integrity and the environmental emissions. Several Big Data Analytics have been tested and presented in this paper, along with different methodologies of input-data pre-conditioning. Results related to the application of the tool on normal operations show a significant impact in terms of down-time reduction and production optimization. The possibility to have alerts and information a few hours in advance gives to the operator the ability to reach the asset operational target, which is not only related to the management of critical events but also to the achievement of the maximum level of production thanks to the definition of an optimal configuration of operating parameters. The tool highlights also the main parameters affecting the prediction suggesting corrective actions to prevent and mitigate risks and occurring critical events. The innovative characteristics of the tool are the ability to take advantage of a huge amount of field data and to simulate complex phenomenon through mathematical-statistical methodologies, based on machine learning algorithms. Thanks to this innovative approach, it is possible to quickly predict possible hazardous events and consequently find the optimum asset configuration. This produces positive effects in the field short-term production optimization and the long-term maintenance
{"title":"Big Data Advanced Anlytics to Forecast Operational Upsets in Upstream Production System","authors":"Luca Cadei, M. Montini, Fabio Landi, F. Porcelli, V. Michetti, M. Origgi, Marco Tonegutti, S. Duranton","doi":"10.2118/193190-MS","DOIUrl":"https://doi.org/10.2118/193190-MS","url":null,"abstract":"\u0000 This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to mitigate or avoid operational issues, maximizing the asset value, while maintaining the highest safety and environmental quality. This in-house developed tool is based on big data analytics techniques such as machine and deep learning algorithms.\u0000 The workflow developed allows predicting future events and the related influencing variables. This is done thanks to a powerful machine-learning algorithm specifically selected for the physical problem analyzed. The inputs come from a heterogeneous data-lake, composed by historical data, real-time series, maintenance reports, chemical analysis and operator experience. The workflow developed starts processing and enhancing this huge amount of data in order to train and validate the selected algorithm. Finally, the tool is fed with real-time data from the field, predicting potential events and prescribing possible actions to avoid problems that jeopardize the production and the integrity of the asset.\u0000 The tool has demonstrated the capability to predict in advance operational upsets occurring within the entire production system avoiding issues, maximizing the field availability. The case illustrated in this paper focuses the attention on the process section of an upstream oil field. In particular, process upsets of the sweetening unit, such as H2S out of specification, are analyzed since they affect not only the field production, but also the asset integrity and the environmental emissions. Several Big Data Analytics have been tested and presented in this paper, along with different methodologies of input-data pre-conditioning. Results related to the application of the tool on normal operations show a significant impact in terms of down-time reduction and production optimization. The possibility to have alerts and information a few hours in advance gives to the operator the ability to reach the asset operational target, which is not only related to the management of critical events but also to the achievement of the maximum level of production thanks to the definition of an optimal configuration of operating parameters. The tool highlights also the main parameters affecting the prediction suggesting corrective actions to prevent and mitigate risks and occurring critical events.\u0000 The innovative characteristics of the tool are the ability to take advantage of a huge amount of field data and to simulate complex phenomenon through mathematical-statistical methodologies, based on machine learning algorithms. Thanks to this innovative approach, it is possible to quickly predict possible hazardous events and consequently find the optimum asset configuration. This produces positive effects in the field short-term production optimization and the long-term maintenance","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81086029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fluid rates and bottom-hole flowing pressure of the wells are essential parameters in the petroleum industry. The need of accurate readings of these measurements are necessary for many calculations such as gas-lift optimization, field monitoring and depletion plans. Predicting these parameters without running in hole has a good impact on reducing the intervention risk and on organization financials by saving time and money. Huge number of correlations are used to estimate these parameters. These correlations need the values of different parameters that are not accurately found. Therefore, an artificial neural network (ANN) model was built from exported data set of PROSPER1 software, production logging tool (PLT), and test separator data. The ANN model was trained and tested by the PROSPER1 extracted data. Then, a number of test points gathered from the PLT reports validated the ANN model. The developed ANN model results in an accurate prediction of the well flowing bottom-hole pressure and well fluid rate. These readings of each well are used to build an integrated production model (IPM) using GAP2 software to apply different gas-lift optimization scenarios.
{"title":"Soft Computation Application: Utilizing Artificial Neural Network to Predict the Fluid Rate and Bottom Hole Flowing Pressure for Gas-lifted Oil Wells","authors":"M. Bahaa, E. Shokir, I. Mahgoub","doi":"10.2118/193052-MS","DOIUrl":"https://doi.org/10.2118/193052-MS","url":null,"abstract":"\u0000 The fluid rates and bottom-hole flowing pressure of the wells are essential parameters in the petroleum industry. The need of accurate readings of these measurements are necessary for many calculations such as gas-lift optimization, field monitoring and depletion plans. Predicting these parameters without running in hole has a good impact on reducing the intervention risk and on organization financials by saving time and money. Huge number of correlations are used to estimate these parameters. These correlations need the values of different parameters that are not accurately found. Therefore, an artificial neural network (ANN) model was built from exported data set of PROSPER1 software, production logging tool (PLT), and test separator data. The ANN model was trained and tested by the PROSPER1 extracted data. Then, a number of test points gathered from the PLT reports validated the ANN model. The developed ANN model results in an accurate prediction of the well flowing bottom-hole pressure and well fluid rate. These readings of each well are used to build an integrated production model (IPM) using GAP2 software to apply different gas-lift optimization scenarios.","PeriodicalId":11208,"journal":{"name":"Day 2 Tue, November 13, 2018","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77738293","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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