Muzaffar Mohamdally, M. Soroush, M. Zeidouni, D. Alexander, Donnie Boodlal
Fault transmissibility and leakage have significant implications for field development during both primary and post-primary recovery. Whether the fault is sealing or not can directly determine the sweep efficiency and the fate of injected fluids. In addition, fault transmissivity affect the accuracy of in-place volume calculations from material balance techniques. In this paper dynamic data was used to determine transmissibility and leakage of the faults via Capacitance Model (CM). The CM has been developed from linear productivity model and material balance equation. Its inputs are production/injection rates and bottomhole pressure data (if available). The CM has weight factor for each well pair which determines the degree of connectivity between that pair. These weight factors were used and correlated to the fault transmissibility in this paper. Also, the CM was modified to incorporate the leakage in the system. New term, called leakage factor, was added for each well in the equation. The model was examined through applying to several synthetic field data generated by CMG software. In synthetic fields, different faults with different throw and transmissibility were built and across the fault transmissibility was evaluated by the model. For creating leaking fault, upward leakage and flow along the fault were examined. Estimated zero leakage factor means no leakage and one means maximum leakage for the wells. The leakage factors not only identified where the leakage was happening, but also determined the amount of leakage by multiplying leakage factor to the net accumulation. In reservoirs with complex geology and several faults, commonly encountered in Trinidad, all geological and geophysical complexities might not be accurately known. Using alternative methods such as the CM can complement, validate or better determine fault properties such as leakage and transmissibility for proper application of EOR schemes.
{"title":"Fault Leakage Assessment Using the Capacitance Model","authors":"Muzaffar Mohamdally, M. Soroush, M. Zeidouni, D. Alexander, Donnie Boodlal","doi":"10.2118/191250-MS","DOIUrl":"https://doi.org/10.2118/191250-MS","url":null,"abstract":"\u0000 Fault transmissibility and leakage have significant implications for field development during both primary and post-primary recovery. Whether the fault is sealing or not can directly determine the sweep efficiency and the fate of injected fluids. In addition, fault transmissivity affect the accuracy of in-place volume calculations from material balance techniques. In this paper dynamic data was used to determine transmissibility and leakage of the faults via Capacitance Model (CM).\u0000 The CM has been developed from linear productivity model and material balance equation. Its inputs are production/injection rates and bottomhole pressure data (if available). The CM has weight factor for each well pair which determines the degree of connectivity between that pair. These weight factors were used and correlated to the fault transmissibility in this paper. Also, the CM was modified to incorporate the leakage in the system. New term, called leakage factor, was added for each well in the equation.\u0000 The model was examined through applying to several synthetic field data generated by CMG software. In synthetic fields, different faults with different throw and transmissibility were built and across the fault transmissibility was evaluated by the model. For creating leaking fault, upward leakage and flow along the fault were examined. Estimated zero leakage factor means no leakage and one means maximum leakage for the wells. The leakage factors not only identified where the leakage was happening, but also determined the amount of leakage by multiplying leakage factor to the net accumulation.\u0000 In reservoirs with complex geology and several faults, commonly encountered in Trinidad, all geological and geophysical complexities might not be accurately known. Using alternative methods such as the CM can complement, validate or better determine fault properties such as leakage and transmissibility for proper application of EOR schemes.","PeriodicalId":415543,"journal":{"name":"Day 2 Tue, June 26, 2018","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116077493","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}
Successful delivery of oil and gas development projects are measured against the promise of an expected production outcome, delivered safely within a scheduled time and budget. This promise is generally based on production forecasts and cost/schedule estimates, with the aim to incorporate the impact of risks and uncertainties on the project. While there are established methodologies for incorporating uncertainties into production forecasts and risks into cost and schedule estimates, there is no established methodology for quantifying the impact of subsurface, drilling or operational risks on production forecasts within foreseen range of cost and schedule. As a result, these risks are often either ignored or incorrectly accounted for as an arbitrary percentage discount on forecasted volume. The objective of this paper is to propose a clear methodology to categorize, quantify and incorporate these risks in forecasts, provide a basis for robust production forecasting and drive better business decisions. Three main risk types are defined in this methodology under two categories: Execution risks and Operational risks. Execution risks are defined as the risks occurring at the time of execution comprising of Subsurface and Mechanical risk types. Subsurface risk is probability that the encountered subsurface outcome is poorer than considered in the uncertainty ranges, e.g. depleted, swept, compartmentalized or with unexpected fluids/contaminants. Mechanical risk is the probability of unsuccessful drilling, completion or intervention of the well as per the development plan, e.g. due to borehole collapse, well loss or completion failure. Operational risks exist throughout the production lifetime and are defined as the probability of premature failure of the well or shut-down of the facility before producing its Estimated Ultimate Recovery (EUR), due to completion failure, well and facility integrity challenges. The Execution risks are expressed as Chance of Success (CoS) against the risk and modelled using a Bernoulli distribution. The Operational risks are defined using a CoS and a distribution function derived based on statistics of historical failures observed in regional/analogous field(s). The risks are rolled-up in a probabilistic decision tree analysis along with the low, mid and high subsurface outcomes. The P90, P50 and P10 cases are identified from multiple realizations based on production rates and EUR outcomes, and deterministic equivalents of each outcome are selected based on possible scenarios. The Development Well Risking methodology incorporates multiple risks into production forecasts, and introduces a more robust approach towards forecast adjustments across the industry. Furthermore, the methodology is used to better evaluate competitive scopes and assist with decision making processes. The risking also provides a basis for justification of base protection projects or activities that de-risk base case production but provide no direc
{"title":"Development Well Risking of Production Forecasts","authors":"P. Nurafza, Khem Budhram, Russell Julier","doi":"10.2118/191211-MS","DOIUrl":"https://doi.org/10.2118/191211-MS","url":null,"abstract":"\u0000 Successful delivery of oil and gas development projects are measured against the promise of an expected production outcome, delivered safely within a scheduled time and budget. This promise is generally based on production forecasts and cost/schedule estimates, with the aim to incorporate the impact of risks and uncertainties on the project. While there are established methodologies for incorporating uncertainties into production forecasts and risks into cost and schedule estimates, there is no established methodology for quantifying the impact of subsurface, drilling or operational risks on production forecasts within foreseen range of cost and schedule. As a result, these risks are often either ignored or incorrectly accounted for as an arbitrary percentage discount on forecasted volume. The objective of this paper is to propose a clear methodology to categorize, quantify and incorporate these risks in forecasts, provide a basis for robust production forecasting and drive better business decisions.\u0000 Three main risk types are defined in this methodology under two categories: Execution risks and Operational risks. Execution risks are defined as the risks occurring at the time of execution comprising of Subsurface and Mechanical risk types. Subsurface risk is probability that the encountered subsurface outcome is poorer than considered in the uncertainty ranges, e.g. depleted, swept, compartmentalized or with unexpected fluids/contaminants. Mechanical risk is the probability of unsuccessful drilling, completion or intervention of the well as per the development plan, e.g. due to borehole collapse, well loss or completion failure. Operational risks exist throughout the production lifetime and are defined as the probability of premature failure of the well or shut-down of the facility before producing its Estimated Ultimate Recovery (EUR), due to completion failure, well and facility integrity challenges.\u0000 The Execution risks are expressed as Chance of Success (CoS) against the risk and modelled using a Bernoulli distribution. The Operational risks are defined using a CoS and a distribution function derived based on statistics of historical failures observed in regional/analogous field(s). The risks are rolled-up in a probabilistic decision tree analysis along with the low, mid and high subsurface outcomes. The P90, P50 and P10 cases are identified from multiple realizations based on production rates and EUR outcomes, and deterministic equivalents of each outcome are selected based on possible scenarios.\u0000 The Development Well Risking methodology incorporates multiple risks into production forecasts, and introduces a more robust approach towards forecast adjustments across the industry. Furthermore, the methodology is used to better evaluate competitive scopes and assist with decision making processes. The risking also provides a basis for justification of base protection projects or activities that de-risk base case production but provide no direc","PeriodicalId":415543,"journal":{"name":"Day 2 Tue, June 26, 2018","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131717125","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}
Abdullah M. Al Moajil, Ahmed G. Alghizzi, Ali Alsalem, Sajjad Aldarweesh
Fracturing fluids are normally injected at high rates and pressures to break the reservoir rock, where proppants ideally are suspended during fluid injection. High strength ceramic proppants are used to overcome hash environments (i.e., high closure stress and temperatures). Advancements in proppant manufacturing further added several characteristics to the proppants, such as self-suspending, multi-phase flow enhancer, and multifactional proppants. The objectives of this study were to compare the performance of HSP and ULW ceramic proppants though proppant characterization, wettability measurements, settling behavior, acid solubility, proppant pack conductivity, and proppant crush resistance. Fracture cell was used to measure the proppant pack conductivity. Proppant crush resistance was conducted using hydraulic uni-axial loading frame. XRD and XRF were used to characterize proppant samples. Solubility in HCl solutions was examined. Elemental analysis was conducted using ICP. Light transmission and backscattering technique was used to compare the settling behavior of proppant samples. Drop Shape Analyzer was used to measure the contact angle on the surface of proppant samples. The highest performance proppant among the five-examined proppants was proppant P-1. This was based on the conductivity values obtained, the correlation between conductivity and fines generated, settling behavior, and solubility in HCl acids. Proppant P-5 exhibited non-wetted properties for both water and condensate fluids. ULW proppants (i.e., P-7 and P-8) showed significantly improved suspension properties over the examined HSP proppants. The solubility of the HSP proppants in HCl acid depended on the acid concentration, soaking time, surface area. The solubilities obtained was up to 10 wt% in concentrated HCl acids. High concentrations of Fe were observed in concentrated acid solution (i.e. ~1800 mg/l). Proppant pack conductivity values for examined proppants were relatively similar except for proppants P-3 and P-5.A linear correlation was found between wt% of fines generated and proppant pack conductivity.
{"title":"Advanced HSP Ceramic Proppants— An Evaluation and Effect of Fines on Proppant Pack Conductivity","authors":"Abdullah M. Al Moajil, Ahmed G. Alghizzi, Ali Alsalem, Sajjad Aldarweesh","doi":"10.2118/191182-MS","DOIUrl":"https://doi.org/10.2118/191182-MS","url":null,"abstract":"\u0000 Fracturing fluids are normally injected at high rates and pressures to break the reservoir rock, where proppants ideally are suspended during fluid injection. High strength ceramic proppants are used to overcome hash environments (i.e., high closure stress and temperatures). Advancements in proppant manufacturing further added several characteristics to the proppants, such as self-suspending, multi-phase flow enhancer, and multifactional proppants. The objectives of this study were to compare the performance of HSP and ULW ceramic proppants though proppant characterization, wettability measurements, settling behavior, acid solubility, proppant pack conductivity, and proppant crush resistance.\u0000 Fracture cell was used to measure the proppant pack conductivity. Proppant crush resistance was conducted using hydraulic uni-axial loading frame. XRD and XRF were used to characterize proppant samples. Solubility in HCl solutions was examined. Elemental analysis was conducted using ICP. Light transmission and backscattering technique was used to compare the settling behavior of proppant samples. Drop Shape Analyzer was used to measure the contact angle on the surface of proppant samples.\u0000 The highest performance proppant among the five-examined proppants was proppant P-1. This was based on the conductivity values obtained, the correlation between conductivity and fines generated, settling behavior, and solubility in HCl acids. Proppant P-5 exhibited non-wetted properties for both water and condensate fluids. ULW proppants (i.e., P-7 and P-8) showed significantly improved suspension properties over the examined HSP proppants. The solubility of the HSP proppants in HCl acid depended on the acid concentration, soaking time, surface area. The solubilities obtained was up to 10 wt% in concentrated HCl acids. High concentrations of Fe were observed in concentrated acid solution (i.e. ~1800 mg/l). Proppant pack conductivity values for examined proppants were relatively similar except for proppants P-3 and P-5.A linear correlation was found between wt% of fines generated and proppant pack conductivity.","PeriodicalId":415543,"journal":{"name":"Day 2 Tue, June 26, 2018","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131609725","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}
Injection and production historical data are easily accessible and using them does not incur the costs of running field tests. The capacitance model (CM), an analytical model based on injection and production data, has recently been applied successfully in several field cases. The CM has two outcomes, rate prediction and well to well connectivity evaluation and primarily derived for waterflood period. This paper modified this model for primary production period. The CM has been developed from linear productivity model and material balance equation and predicts the total production rate of each producer as a function of the injection rates of all injectors in the system and the bottomhole pressures (BHPs) of all producers. In this paper the CM is modified based on two methods, Pseudo Injectors and BHP methods. Pseudo Injectors method is used for well to well connectivity assessment and BHP method is used for production prediction. The modified CM was applied for several synthetic field examples and one Iranian oil reservoir. The results of synthetic fields showed that the modified CM can assess the interwell connectivity, reservoir heterogeneity, strength of aquifer, and wellbore productivity in primary production period. In addition, the modified CM can predict production rate and determine suitable areas of future IOR application. The results of modified CM on Iranian field assessed the effect of aquifer in the area and evaluated the degree of heterogeneity of the sands around the producers. Unlike simulation-based methods, the CM does not require geological and geophysical data to generate the initial model. Developed modified CM can be applied before IOR implementation to assess reservoir continuity and manage future IOR strategies such as well pattern and amount of injected fluid.
{"title":"Application of the Capacitance Model in Primary Production Period before IOR Implementation","authors":"M. Soroush, M. Rasaei","doi":"10.2118/191236-MS","DOIUrl":"https://doi.org/10.2118/191236-MS","url":null,"abstract":"\u0000 Injection and production historical data are easily accessible and using them does not incur the costs of running field tests. The capacitance model (CM), an analytical model based on injection and production data, has recently been applied successfully in several field cases. The CM has two outcomes, rate prediction and well to well connectivity evaluation and primarily derived for waterflood period. This paper modified this model for primary production period.\u0000 The CM has been developed from linear productivity model and material balance equation and predicts the total production rate of each producer as a function of the injection rates of all injectors in the system and the bottomhole pressures (BHPs) of all producers. In this paper the CM is modified based on two methods, Pseudo Injectors and BHP methods. Pseudo Injectors method is used for well to well connectivity assessment and BHP method is used for production prediction.\u0000 The modified CM was applied for several synthetic field examples and one Iranian oil reservoir. The results of synthetic fields showed that the modified CM can assess the interwell connectivity, reservoir heterogeneity, strength of aquifer, and wellbore productivity in primary production period. In addition, the modified CM can predict production rate and determine suitable areas of future IOR application. The results of modified CM on Iranian field assessed the effect of aquifer in the area and evaluated the degree of heterogeneity of the sands around the producers.\u0000 Unlike simulation-based methods, the CM does not require geological and geophysical data to generate the initial model. Developed modified CM can be applied before IOR implementation to assess reservoir continuity and manage future IOR strategies such as well pattern and amount of injected fluid.","PeriodicalId":415543,"journal":{"name":"Day 2 Tue, June 26, 2018","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114705793","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：