J. Nielsen, K. G. Poulsen, J. H. Christensen, T. Sølling
Mature fields often times surprise with respect to the production from the various wells across reservoir sections. This is for example the case in a tight chalk field that we have used as a case study for newly developed technique that employs oil finger printing in the analysis of production data. A small subset of wells has been found to produce significantly better than the remainder and we set out to explore whether the root cause is that there is a connection to higher lying reservoir sections through natural or artificial fractures. This was done with advanced analytical chemistry (GC-MS) and a principal component analysis to map differences between key constituents of the oil from wells across the reservoir section. The comparative parameters are mainly derived from biomarker properties but we also developed a way to directly include production numbers. The approach provides means to correlate the molecular properties of the oil with the production and the general composition that determines density and adhesive (to the rock) properties. Thus, the results provide a new angle on the flow properties of the oil and on the charging history of the reservoir. It is clear from the analysis that the subset of wells does not produce better because of a connection to an upper reservoir section that contributes to the production with oil of a different composition because the molecular mix is indeed quite similar in each of the investigated wells. It is not possible to rule out that there is a connection to an upper-lying section with oil from the same source. One aspect that does differs across the field is the ratio of heavy versus light molecules within each group of molecules and the results show that the region that produce better has the lighter components. We take that to indicate that the lighter components come from oil that flows better and thus is produced more easily. The reservoir section with the lighter oil also lies higher on the structure and is therefore must likely to have been charged first so part of the favorable production seems to be a matter of "first in" "first out". A GC-MS approach such as the one proposed here is cost-effective, fast and highly promising for future predictions on where to perform infill campaigns because the results are indicative of charging history and flow properties of the oil.
{"title":"Tracing Production with Analytical Chemistry: Can Oil Finger Printing Provide New Answers","authors":"J. Nielsen, K. G. Poulsen, J. H. Christensen, T. Sølling","doi":"10.2118/194916-MS","DOIUrl":"https://doi.org/10.2118/194916-MS","url":null,"abstract":"\u0000 Mature fields often times surprise with respect to the production from the various wells across reservoir sections. This is for example the case in a tight chalk field that we have used as a case study for newly developed technique that employs oil finger printing in the analysis of production data. A small subset of wells has been found to produce significantly better than the remainder and we set out to explore whether the root cause is that there is a connection to higher lying reservoir sections through natural or artificial fractures. This was done with advanced analytical chemistry (GC-MS) and a principal component analysis to map differences between key constituents of the oil from wells across the reservoir section. The comparative parameters are mainly derived from biomarker properties but we also developed a way to directly include production numbers. The approach provides means to correlate the molecular properties of the oil with the production and the general composition that determines density and adhesive (to the rock) properties. Thus, the results provide a new angle on the flow properties of the oil and on the charging history of the reservoir. It is clear from the analysis that the subset of wells does not produce better because of a connection to an upper reservoir section that contributes to the production with oil of a different composition because the molecular mix is indeed quite similar in each of the investigated wells. It is not possible to rule out that there is a connection to an upper-lying section with oil from the same source. One aspect that does differs across the field is the ratio of heavy versus light molecules within each group of molecules and the results show that the region that produce better has the lighter components. We take that to indicate that the lighter components come from oil that flows better and thus is produced more easily. The reservoir section with the lighter oil also lies higher on the structure and is therefore must likely to have been charged first so part of the favorable production seems to be a matter of \"first in\" \"first out\". A GC-MS approach such as the one proposed here is cost-effective, fast and highly promising for future predictions on where to perform infill campaigns because the results are indicative of charging history and flow properties of the oil.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86285945","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}
Abdul-Aziz Bassam, Ghazi Al-Besairi, Sulaiman Al-Dahash, Tomas Sierra, A. Mohamed, K. Heshmat
The demand for digital oil field solutions in artificially lifted wells is higher than ever, especially for wells producing heavy oil with high sand content and gas. A real-time supervisory control and data acquisition solution has been applied in a large-scale thermal pilot for 28 instrumented sucker rod pumping wells in North Kuwait. This paper focuses on the advantages of real-time data acquisition for identifying production-optimization candidates, improving pump performance, and minimizing down time when using intelligent alarms and an analysis engine. Real-time surveillance provided a huge amount of information to be analyzed and discussed by well surveillance and field development teams to determine required actions based on individual well performance. Controller alarms and intelligent configurable alarms in one screen enabled early detection of unexpected/unwanted well behavior, re-investigating well potential, and taking necessary actions. The challenge was to handle heavy oil, sand, and gas production, maintain all wells at optimum running conditions before and after steam injections, and take into consideration the effect that injections would have on nearby wells. Recording in the database a "tracking item" for each well event enabled review and evaluation of the wells and creation of optimization reports. The daily, 24-hour surveillance of the wells resulted in observing common problems/issues on almost all wells and other individual issues for specific wells. The following are examples of problems identified in early stages: Detected wells with gas interference before they reached gas lockDetected wells with high flowline pressure before flowline blockage resulted from sand productionDetected wells with standing valve and/or traveling valve leak—resulting from sand production—before the pump stuck The availability of such a supervisory control and data acquisition (SCADA) system helped in guiding the operations team to further investigate only specific items from the field side to confirm the findings. The ability to remotely control the wells and remotely change configuration of the variable speed drive parameters enabled instant implementation and continuous production optimization. The powerful SCADA solution enabled creating short- and long-term plans and monitoring the behavior of wells while the implementation phase was executed. For the first time in South Ratqa in North Kuwait, the smart field approach was implemented in a thermal pilot using sucker rod pumps; and the results will be used as a reference for the upcoming projects in this area. Real-time monitoring and data storage in a single database with an analysis engine provided fully automated surveillance and the capability of remotely controlling and applying required actions for production optimization.
{"title":"Production Optimization Challenges and Solutions for Heavy Oil - North Kuwait","authors":"Abdul-Aziz Bassam, Ghazi Al-Besairi, Sulaiman Al-Dahash, Tomas Sierra, A. Mohamed, K. Heshmat","doi":"10.2118/194809-MS","DOIUrl":"https://doi.org/10.2118/194809-MS","url":null,"abstract":"\u0000 The demand for digital oil field solutions in artificially lifted wells is higher than ever, especially for wells producing heavy oil with high sand content and gas. A real-time supervisory control and data acquisition solution has been applied in a large-scale thermal pilot for 28 instrumented sucker rod pumping wells in North Kuwait. This paper focuses on the advantages of real-time data acquisition for identifying production-optimization candidates, improving pump performance, and minimizing down time when using intelligent alarms and an analysis engine.\u0000 Real-time surveillance provided a huge amount of information to be analyzed and discussed by well surveillance and field development teams to determine required actions based on individual well performance. Controller alarms and intelligent configurable alarms in one screen enabled early detection of unexpected/unwanted well behavior, re-investigating well potential, and taking necessary actions.\u0000 The challenge was to handle heavy oil, sand, and gas production, maintain all wells at optimum running conditions before and after steam injections, and take into consideration the effect that injections would have on nearby wells.\u0000 Recording in the database a \"tracking item\" for each well event enabled review and evaluation of the wells and creation of optimization reports. The daily, 24-hour surveillance of the wells resulted in observing common problems/issues on almost all wells and other individual issues for specific wells.\u0000 The following are examples of problems identified in early stages: Detected wells with gas interference before they reached gas lockDetected wells with high flowline pressure before flowline blockage resulted from sand productionDetected wells with standing valve and/or traveling valve leak—resulting from sand production—before the pump stuck\u0000 The availability of such a supervisory control and data acquisition (SCADA) system helped in guiding the operations team to further investigate only specific items from the field side to confirm the findings. The ability to remotely control the wells and remotely change configuration of the variable speed drive parameters enabled instant implementation and continuous production optimization. The powerful SCADA solution enabled creating short- and long-term plans and monitoring the behavior of wells while the implementation phase was executed.\u0000 For the first time in South Ratqa in North Kuwait, the smart field approach was implemented in a thermal pilot using sucker rod pumps; and the results will be used as a reference for the upcoming projects in this area. Real-time monitoring and data storage in a single database with an analysis engine provided fully automated surveillance and the capability of remotely controlling and applying required actions for production optimization.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"204 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90488907","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}
W. N. S. Zainuddin, S. X. Xie, N. I. Kechut, B. Kantaatmadja, P. Singer, G. Hirasaki
Nuclear magnetic resonance (NMR) T2 spin-spin relaxation is a well-established technique in petrophysics labs for quantifying bound/free water and pore-size distribution of reservoir rocks. The method has also been used to measure oil and water saturations, and to characterize wettability alterations for oil/water/rock systems. The T2 relaxation distribution measured by hydrogen NMR is the sum of contributions from both oil and water in the core. It is therefore necessary to separate the T2 signals of oil from water. Since deuterium oxide (D2O) does not have a NMR signal at the resonance frequency for hydrogen, brine made with D2O is commonly used as the aqueous phase to determine the oil saturation from NMR. The objective of this work was twofold: (1) to validate the oil saturations in the core with NMR T2 relaxation at connate water saturation (before and after aging) and residual oil saturation after waterflooding; and (2) to investigate the potential hydrogen-deuterium (H-D) ion exchange between rock minerals and D2O. Berea sandstone cores were used along with the crude oil from one of the fields in the Sarawak Basin, Malaysia. The aqueous phase was a synthetic brine made with either deionized water or D2O. Two cores containing the crude oil with D2O brine as the connate (or initial) water were aged at 75eC for up to 65 days. During the aging period, the cores were scanned three times for T2 measurements. The measured T2 volumes (supposedly a measure of the oil volume) of the two cores kept increasing as the aging time increased. However, mass balance indicated that the oil saturation was the same before and after aging. The inconsistent oil saturation measured by NMR indicated that there was H-D ion exchange between the rock minerals and D2O. The cores were then flooded with the fresh D2O brine, after which the residual oil from NMR agreed with that from mass balance, indicating that the fresh D2O had replaced the connate D2O brine affected by H-D ion exchange. Additionally, two cores fully saturated with D2O brine were also measured by NMR before and after aging at 75°C, again confirming the H-D ion exchange between the rock minerals and D2O. Finally, the mixture of the crude oil and D2O was measured by NMR before and after aging at 75°C, indicating that the interactions between the crude oil and D2O increased the T2 relaxation time. The total T2 volume was not affected. This work provides evidence of H-D ion exchange between rock minerals and D2O at elevated temperature. It is recommended that such interactions between the rock minerals and D2O brine be considered for related tests, especially when elevated temperature is involved.
{"title":"Hydrogen-Deuterium Exchange Between Rock Minerals and D2O","authors":"W. N. S. Zainuddin, S. X. Xie, N. I. Kechut, B. Kantaatmadja, P. Singer, G. Hirasaki","doi":"10.2118/194978-MS","DOIUrl":"https://doi.org/10.2118/194978-MS","url":null,"abstract":"\u0000 Nuclear magnetic resonance (NMR) T2 spin-spin relaxation is a well-established technique in petrophysics labs for quantifying bound/free water and pore-size distribution of reservoir rocks. The method has also been used to measure oil and water saturations, and to characterize wettability alterations for oil/water/rock systems. The T2 relaxation distribution measured by hydrogen NMR is the sum of contributions from both oil and water in the core. It is therefore necessary to separate the T2 signals of oil from water. Since deuterium oxide (D2O) does not have a NMR signal at the resonance frequency for hydrogen, brine made with D2O is commonly used as the aqueous phase to determine the oil saturation from NMR.\u0000 The objective of this work was twofold: (1) to validate the oil saturations in the core with NMR T2 relaxation at connate water saturation (before and after aging) and residual oil saturation after waterflooding; and (2) to investigate the potential hydrogen-deuterium (H-D) ion exchange between rock minerals and D2O. Berea sandstone cores were used along with the crude oil from one of the fields in the Sarawak Basin, Malaysia. The aqueous phase was a synthetic brine made with either deionized water or D2O.\u0000 Two cores containing the crude oil with D2O brine as the connate (or initial) water were aged at 75eC for up to 65 days. During the aging period, the cores were scanned three times for T2 measurements. The measured T2 volumes (supposedly a measure of the oil volume) of the two cores kept increasing as the aging time increased. However, mass balance indicated that the oil saturation was the same before and after aging. The inconsistent oil saturation measured by NMR indicated that there was H-D ion exchange between the rock minerals and D2O. The cores were then flooded with the fresh D2O brine, after which the residual oil from NMR agreed with that from mass balance, indicating that the fresh D2O had replaced the connate D2O brine affected by H-D ion exchange.\u0000 Additionally, two cores fully saturated with D2O brine were also measured by NMR before and after aging at 75°C, again confirming the H-D ion exchange between the rock minerals and D2O. Finally, the mixture of the crude oil and D2O was measured by NMR before and after aging at 75°C, indicating that the interactions between the crude oil and D2O increased the T2 relaxation time. The total T2 volume was not affected.\u0000 This work provides evidence of H-D ion exchange between rock minerals and D2O at elevated temperature. It is recommended that such interactions between the rock minerals and D2O brine be considered for related tests, especially when elevated temperature is involved.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89948637","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}
Located in south of Eastern Venezuela Basin, Orinoco Oilfield is an onshore heavy oil field in South America. The heavy oil is known for its high content of acids, heavy metals and asphaltenes with a viscosity of 1000-10000mPa·s. According to the reserve report released by PDVSA by the end of 2016, JUNIN Block that is situated in east of Orinoco Oilfield has an OOIP of 178*108bbl. Data of drilled wells and distances between offset horizontal intervals in Orinoco were both studied to improve ultimate production rates. 3-dimension borehole trajectories were designed and the most effective anti-collision measures were taken. After optimziation 8-12 horizontal wells are distributed on one pad. As the horizontal interval extends, the stable production time is prolonged and the accumulative production per well improves. However, the recovery rate stops increasing when the horizontal interval is over 1600m in JUNIN Block. Economically a large space between offset horizontal intervals results in fewer wells and lower costs, but a smaller space contributes to a higher production efficiency per well. If the space exceeds 600m, the accumulative production rate increases much more slightly. A three-dimension well trajectory consists of a vertical interval, an angle building interval, an angle holding interval, an angle building & direction changing interval, a direction turning interval as well as an absolute horizontal interval. Since Petrobras developed the first ever offshore deep reservoir (Lula) by scale in 2006, Brazil has been conducting a progressive campaign targeting hydrocarbons buried under deep water, which contributes to discovery of Lula, Carioca, Jupiter, Buzios, Libra and other giant presalt reservoirs in Santos Basin after Campos Basin, where there are 9 oil fields ranking among the top 20 offshore oil fields in terms of OOIP. By June 2017 over 160×104bbl oil and gas were produced per day in deep water of Santos Basin, taking up 57.1% of the total yield of Campos and Satos. Creep deformation of ultra-thick salt beds, severe loss of limestones, poor drillability of formations and insufficiency of deep water drilling equipment all make drilling and completion challenges more complicated. Mud systems and casing programs are optimized to conquer creep of salt and formation of hydrates due to low downhole temperature. Turbines + impregnated bits are deployed to improve drilling efficiency of siliceous carbonates (Lagoa Feia A Group). Precise control of ECD and efficient LCMs solved engineering challenges caused by narrow density windows (Lagoa Feia B Group and Lagoa Feia C Group).
Orinoco油田位于委内瑞拉东部盆地南部，是南美洲的一个陆上重油油田。重油以酸、重金属和沥青质含量高而闻名，粘度为1000-10000mPa·s。根据PDVSA于2016年底发布的储量报告，位于Orinoco油田东部的JUNIN区块的OOIP为178*108bbl。为了提高最终产量，研究了Orinoco油田的钻井数据和邻距水平段之间的距离。设计了三维井眼轨迹，并采取了最有效的防撞措施。优化后，8-12口水平井分布在一个区块上。随着水平段的延长，稳定生产时间延长，单井累计产量提高。而在JUNIN区块，当水平层距超过1600m时，采收率停止增长。从经济角度来看，邻距水平段之间的较大空间可以减少井数，降低成本，但较小的空间可以提高每口井的生产效率。当空间超过600m时，累计产量增加幅度要小得多。三维井眼轨迹包括垂直井段、造角井段、持角井段、造角变向井段、转向井段和绝对水平井段。自2006年巴西国家石油公司(Petrobras)首次大规模开发海上深层油藏(Lula)以来，巴西一直在开展针对深水油气的活动，在Campos盆地之后，在Santos盆地发现了Lula、Carioca、Jupiter、Buzios、Libra等大型盐下油藏，其中有9个油田在OOIP排名前20位的海上油田。截至2017年6月，Santos盆地深水油气日产量超过160×104bbl，占Campos和Satos总产量的57.1%。超厚盐层的蠕变变形、灰岩的严重损失、地层可钻性差以及深水钻井设备的不足，都使钻井完井挑战更加复杂。泥浆系统和套管方案进行了优化，以克服由于井下温度低而导致的盐蠕变和水合物地层。采用涡轮+浸渍钻头提高碳化硅钻井效率(Lagoa Feia A Group)。精确的ECD控制和高效的lcm解决了窄密度窗口带来的工程挑战(Lagoa Feia B组和Lagoa Feia C组)。
{"title":"Research of Drilling and Completion Technologies for Heavy Oil in Venezuela and Offshore Presalt Hydrocarbons in Brazil","authors":"Jin Fu, Xi Wang, Shunyuan Zhang, Chen Chen","doi":"10.2118/194989-MS","DOIUrl":"https://doi.org/10.2118/194989-MS","url":null,"abstract":"\u0000 Located in south of Eastern Venezuela Basin, Orinoco Oilfield is an onshore heavy oil field in South America. The heavy oil is known for its high content of acids, heavy metals and asphaltenes with a viscosity of 1000-10000mPa·s. According to the reserve report released by PDVSA by the end of 2016, JUNIN Block that is situated in east of Orinoco Oilfield has an OOIP of 178*108bbl.\u0000 Data of drilled wells and distances between offset horizontal intervals in Orinoco were both studied to improve ultimate production rates. 3-dimension borehole trajectories were designed and the most effective anti-collision measures were taken.\u0000 After optimziation 8-12 horizontal wells are distributed on one pad. As the horizontal interval extends, the stable production time is prolonged and the accumulative production per well improves. However, the recovery rate stops increasing when the horizontal interval is over 1600m in JUNIN Block. Economically a large space between offset horizontal intervals results in fewer wells and lower costs, but a smaller space contributes to a higher production efficiency per well. If the space exceeds 600m, the accumulative production rate increases much more slightly. A three-dimension well trajectory consists of a vertical interval, an angle building interval, an angle holding interval, an angle building & direction changing interval, a direction turning interval as well as an absolute horizontal interval.\u0000 Since Petrobras developed the first ever offshore deep reservoir (Lula) by scale in 2006, Brazil has been conducting a progressive campaign targeting hydrocarbons buried under deep water, which contributes to discovery of Lula, Carioca, Jupiter, Buzios, Libra and other giant presalt reservoirs in Santos Basin after Campos Basin, where there are 9 oil fields ranking among the top 20 offshore oil fields in terms of OOIP. By June 2017 over 160×104bbl oil and gas were produced per day in deep water of Santos Basin, taking up 57.1% of the total yield of Campos and Satos.\u0000 Creep deformation of ultra-thick salt beds, severe loss of limestones, poor drillability of formations and insufficiency of deep water drilling equipment all make drilling and completion challenges more complicated. Mud systems and casing programs are optimized to conquer creep of salt and formation of hydrates due to low downhole temperature. Turbines + impregnated bits are deployed to improve drilling efficiency of siliceous carbonates (Lagoa Feia A Group). Precise control of ECD and efficient LCMs solved engineering challenges caused by narrow density windows (Lagoa Feia B Group and Lagoa Feia C Group).","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85789913","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}
Standard Rock-Eval pyrolysis is commonly used to estimate the thermal maturity of source rocks. However, measuring the maturity of overmature samples with high Tmax values (> 470°C) is very challenging due to the weak development of S2 peaks. Moreover, measuring the vitrinite reflectance of dispersed organic matter high thermal maturity samples is commonly used when the Tmax (°C) of the sample is unreliable. Nevertheless, vitrinite assemblages are very rare/absent in marine samples particularly in marlstones or pre-Carboniferous source rocks. The current study addresses a new thermal maturity parameter that used the carbon monoxide CO released during Rock Eval-6 oxidations. A total of 14 marine source rock samples were analyzed by Rock Eval-6 to assess their generative potential. The samples range in Tmax from 420° to 475°C indicating wide thermal maturity range from immature to overmature. During Rock-Eval analyses, CO released from the kerogens and their peak temperature (Tco) was recorded. A strong positive correlation was observed between the Tmax and the Tco (r=0.94). Note that the CO is released from the organic oxygen compounds that are none/or less liable compared to pure hydrocarbon compounds. Thus, Tco is more reliable than Tmax in assessing high thermal maturity levels. The new method provides a robust and quick interpretation of high thermal maturity source rocks especially for pre-Carboniferous samples that lack a well-devolved S2 peak. Carbon monoxide generation is not affected by carbonate decay to CO2 and is also not affected by contamination used in drilling fluids. Testing of different source rocks is needed to establish this further and to improve the trend observed.
{"title":"Overmature and Vitrinite-Barren Source Rocks: A Novel Thermal Maturity Parameter","authors":"Sebastian Henderson, B. Ghassal","doi":"10.2118/194946-MS","DOIUrl":"https://doi.org/10.2118/194946-MS","url":null,"abstract":"\u0000 Standard Rock-Eval pyrolysis is commonly used to estimate the thermal maturity of source rocks. However, measuring the maturity of overmature samples with high Tmax values (> 470°C) is very challenging due to the weak development of S2 peaks. Moreover, measuring the vitrinite reflectance of dispersed organic matter high thermal maturity samples is commonly used when the Tmax (°C) of the sample is unreliable. Nevertheless, vitrinite assemblages are very rare/absent in marine samples particularly in marlstones or pre-Carboniferous source rocks. The current study addresses a new thermal maturity parameter that used the carbon monoxide CO released during Rock Eval-6 oxidations.\u0000 A total of 14 marine source rock samples were analyzed by Rock Eval-6 to assess their generative potential. The samples range in Tmax from 420° to 475°C indicating wide thermal maturity range from immature to overmature. During Rock-Eval analyses, CO released from the kerogens and their peak temperature (Tco) was recorded. A strong positive correlation was observed between the Tmax and the Tco (r=0.94). Note that the CO is released from the organic oxygen compounds that are none/or less liable compared to pure hydrocarbon compounds. Thus, Tco is more reliable than Tmax in assessing high thermal maturity levels.\u0000 The new method provides a robust and quick interpretation of high thermal maturity source rocks especially for pre-Carboniferous samples that lack a well-devolved S2 peak. Carbon monoxide generation is not affected by carbonate decay to CO2 and is also not affected by contamination used in drilling fluids. Testing of different source rocks is needed to establish this further and to improve the trend observed.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87693457","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}
Amjed Hassan, Abdulaziz Al-Majed, M. Mahmoud, S. Elkatatny, A. Abdulraheem
Oil is considered one of the main drivers that affects the world economy and a key factor in its continuous development. Several operations are used to ensure continues oil production, these operations include; exploration, drilling, production, and reservoir management. Numerous uncertainties and complexities are involved in those operations, which reduce the production performance and increase the operational cost. Several attempts were reported to predict the performance of oil production systems using different approaches, including analytical and numerical methods. However, severe estimation errors and significant deviations were observed between the predicted results and actual field data. This could be due to the different assumptions used to simplify the problems. Therefore, searching for quick and rigorous models to evaluate the oil-production system and anticipate production problems is highly needed. This paper presents a new application of artificial intelligent (AI) techniques to determine the efficiency of several operations including; drilling, production and reservoir performance. For each operation, the most common conditions were applied to develop and evaluate the model reliability. The developed models investigate the significance of different well and reservoir configurations on the system performance. Parameters such as, reservoir permeability, drainage size, wellbore completions, hydrocarbon production rate and choke performance were studied. The primary oil production and enhanced oil recovery (EOR) operations were considered as well as the stimulation processes. Actual data from several oil-fields were used to develop and validate the intelligent models. The novelty of this paper is that the proposed models are reliable and outperform the current methods. This work introduces an effective approach for estimating the performance of oil production system and refine the current numerical or analytical models to improve the reservoir managements.
{"title":"Improved Predictions in Oil Operations Using Artificial Intelligent Techniques","authors":"Amjed Hassan, Abdulaziz Al-Majed, M. Mahmoud, S. Elkatatny, A. Abdulraheem","doi":"10.2118/194994-MS","DOIUrl":"https://doi.org/10.2118/194994-MS","url":null,"abstract":"\u0000 Oil is considered one of the main drivers that affects the world economy and a key factor in its continuous development. Several operations are used to ensure continues oil production, these operations include; exploration, drilling, production, and reservoir management. Numerous uncertainties and complexities are involved in those operations, which reduce the production performance and increase the operational cost.\u0000 Several attempts were reported to predict the performance of oil production systems using different approaches, including analytical and numerical methods. However, severe estimation errors and significant deviations were observed between the predicted results and actual field data. This could be due to the different assumptions used to simplify the problems. Therefore, searching for quick and rigorous models to evaluate the oil-production system and anticipate production problems is highly needed.\u0000 This paper presents a new application of artificial intelligent (AI) techniques to determine the efficiency of several operations including; drilling, production and reservoir performance. For each operation, the most common conditions were applied to develop and evaluate the model reliability. The developed models investigate the significance of different well and reservoir configurations on the system performance. Parameters such as, reservoir permeability, drainage size, wellbore completions, hydrocarbon production rate and choke performance were studied. The primary oil production and enhanced oil recovery (EOR) operations were considered as well as the stimulation processes. Actual data from several oil-fields were used to develop and validate the intelligent models.\u0000 The novelty of this paper is that the proposed models are reliable and outperform the current methods. This work introduces an effective approach for estimating the performance of oil production system and refine the current numerical or analytical models to improve the reservoir managements.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87705144","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. Sharaf, P. Bangert, Mohamed Fardan, Khalil Alqassab, M. Abubakr, Mahmood Ahmed
A beam or a sucker rod pump is an artificial-lift pumping system using a surface power source to drive a downhole pump assembly. A beam and crank assembly creates reciprocating motion in a sucker-rod string that connects to the downhole pump assembly. The pump contains a plunger and valve assembly to convert the reciprocating motion to vertical fluid movement. A dynamometer is a diagnostic device used on sucker rod pumped wells that measures the load on the top rod and plots this load in relation to the polished rod position as the pumping unit moves through each stroke cycle. The analysis of the dynamometer card data delivers valuable insights on the status of the pump and indicates if future actions are required. In practice, the load versus displacement plot shape can be visually categorized in different classes where each shape has a specific meaning and indicates certain operating conditions. Machine learning algorithms are computing systems that learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. During a period of approximately two (2) months, we collected 5,380,163 different cards from 297 beam pumps deployed in the Bahrain Field using the Supervisory Control and Data Acquisition (SCADA) system with an Open Platform Communication (OPC) interface. 35,292 cards are manually labelled by experts into twelve (12) classes. The dataset is split into 80% training and 20% holdout datasets. A training dataset is split into 5-fold cross validation. Different machine learning algorithms are evaluated predicting pump card class and their performance is compared. The top performing model, Gradient Boosting Machines (GBM) Classifier, achieves 99.98% accuracy in cross validation and 100% accuracy on holdout dataset without any extensive feature engineering. This paper explains the steps taken to improve surveillance of beam pumps using dynamometer card data and machine learning techniques and the lessons learned from executing the first Artificial Intelligence (AI) project within Tatweer Petroleum.
{"title":"Beam Pump Dynamometer Card Classification Using Machine Learning","authors":"S. Sharaf, P. Bangert, Mohamed Fardan, Khalil Alqassab, M. Abubakr, Mahmood Ahmed","doi":"10.2118/194949-MS","DOIUrl":"https://doi.org/10.2118/194949-MS","url":null,"abstract":"A beam or a sucker rod pump is an artificial-lift pumping system using a surface power source to drive a downhole pump assembly. A beam and crank assembly creates reciprocating motion in a sucker-rod string that connects to the downhole pump assembly. The pump contains a plunger and valve assembly to convert the reciprocating motion to vertical fluid movement. A dynamometer is a diagnostic device used on sucker rod pumped wells that measures the load on the top rod and plots this load in relation to the polished rod position as the pumping unit moves through each stroke cycle.\u0000 The analysis of the dynamometer card data delivers valuable insights on the status of the pump and indicates if future actions are required. In practice, the load versus displacement plot shape can be visually categorized in different classes where each shape has a specific meaning and indicates certain operating conditions. Machine learning algorithms are computing systems that learn to perform tasks by considering examples, generally without being programmed with any task-specific rules. During a period of approximately two (2) months, we collected 5,380,163 different cards from 297 beam pumps deployed in the Bahrain Field using the Supervisory Control and Data Acquisition (SCADA) system with an Open Platform Communication (OPC) interface. 35,292 cards are manually labelled by experts into twelve (12) classes. The dataset is split into 80% training and 20% holdout datasets. A training dataset is split into 5-fold cross validation. Different machine learning algorithms are evaluated predicting pump card class and their performance is compared. The top performing model, Gradient Boosting Machines (GBM) Classifier, achieves 99.98% accuracy in cross validation and 100% accuracy on holdout dataset without any extensive feature engineering.\u0000 This paper explains the steps taken to improve surveillance of beam pumps using dynamometer card data and machine learning techniques and the lessons learned from executing the first Artificial Intelligence (AI) project within Tatweer Petroleum.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88276435","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}
Approximately 20% of all oilwells in the world use a beam pump to raise crude oil to the surface. The proper maintenance of these pumps is thus an important issue in oilfield operations. We wish to know, preferably before the failure, what is wrong with the pump. Maintenance issues on the downhole part of a beam pump can be reliably diagnosed from a plot of the displacement and load on the traveling valve; a diagram known as a dynamometer card. We demonstrate in this paper that this analysis can be fully automated using machine learning techniques that teach themselves to recognize various classes of damage in advance of the failure. We use a dataset of of 35292 sample cards drawn from 299 beam pumps in the Bahrain oilfield. We can detect 11 different damage classes from each other and from the normal class with an accuracy of 99.9%. This high accuracy makes it possible to automatically diagnose beam pumps in real-time and for the maintenance crew to focus on fixing pumps instead of monitoring them, which increases overall oil yield and decreases environmental impact.
{"title":"Diagnosing and Predicting Problems with Rod Pumps using Machine Learning","authors":"P. Bangert","doi":"10.2118/194993-MS","DOIUrl":"https://doi.org/10.2118/194993-MS","url":null,"abstract":"\u0000 Approximately 20% of all oilwells in the world use a beam pump to raise crude oil to the surface. The proper maintenance of these pumps is thus an important issue in oilfield operations. We wish to know, preferably before the failure, what is wrong with the pump. Maintenance issues on the downhole part of a beam pump can be reliably diagnosed from a plot of the displacement and load on the traveling valve; a diagram known as a dynamometer card. We demonstrate in this paper that this analysis can be fully automated using machine learning techniques that teach themselves to recognize various classes of damage in advance of the failure. We use a dataset of of 35292 sample cards drawn from 299 beam pumps in the Bahrain oilfield. We can detect 11 different damage classes from each other and from the normal class with an accuracy of 99.9%. This high accuracy makes it possible to automatically diagnose beam pumps in real-time and for the maintenance crew to focus on fixing pumps instead of monitoring them, which increases overall oil yield and decreases environmental impact.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75311434","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}
Basirudin Djamaluddin, P. Prabhakar, Baburaj James, Anas Muzakir, Hussain AlMayad
Real-time data stream in the format of WITSML which can have frequency as low as 1 Hz is one of the best candidate to produce KPIs for the drilling operation activity. The KPIs generated from this calculation will have a relationship with other information from other data sources, known as metadata. The question is how can this KPI information be utilized for further analysis, wider/more complex analysis process which needs to be combined with metadata? An OLTP model is not the recommended model for data analytics but OLAP is. Another question is how will this data be stored in terms of the physical storage? We argue to use column-oriented for the physical storage which can perform analytical queries 10x to 30x faster than the row-oriented storage. The implementation of an OLAP model for storing KPIs data is proven to improve the performance of the analytical query significantly and combined with the implementation of column-oriented in the OLAP model improves more performance. This concludes that the implementation of OLAP with column-oriented data model can be used as the solid foundation for storing KPI data.
{"title":"Real-Time Drilling Operation Activity Analysis Data Modelling with Multidimensional Approach and Column-Oriented Storage","authors":"Basirudin Djamaluddin, P. Prabhakar, Baburaj James, Anas Muzakir, Hussain AlMayad","doi":"10.2118/194701-MS","DOIUrl":"https://doi.org/10.2118/194701-MS","url":null,"abstract":"\u0000 Real-time data stream in the format of WITSML which can have frequency as low as 1 Hz is one of the best candidate to produce KPIs for the drilling operation activity. The KPIs generated from this calculation will have a relationship with other information from other data sources, known as metadata.\u0000 The question is how can this KPI information be utilized for further analysis, wider/more complex analysis process which needs to be combined with metadata? An OLTP model is not the recommended model for data analytics but OLAP is. Another question is how will this data be stored in terms of the physical storage? We argue to use column-oriented for the physical storage which can perform analytical queries 10x to 30x faster than the row-oriented storage.\u0000 The implementation of an OLAP model for storing KPIs data is proven to improve the performance of the analytical query significantly and combined with the implementation of column-oriented in the OLAP model improves more performance. This concludes that the implementation of OLAP with column-oriented data model can be used as the solid foundation for storing KPI data.","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90883510","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}
One of the primary functions of Saudi Aramco Gas-Oil Separation Plants (also known as GOSPs) is to separate emulsified water from the crude. The water is typically highly concentrated with salt, so crude desalting is required to meet the standard quality specifications. GOSPs are typically designed with standard Proportional-Integral-Derivative (PID) controllers to control demulsifier and wash water flow for injection into wet crude. Demulsifier and wash water injection rates are normally left to operator judgement. The challenge with manual adjustment of flowrate is the high risk of overdosing or underdosing as there are several variables that impact the required demulsifier and wash water rates. Overdosing will result in wastage of demulsifier and wash water and higher operating expenditures. Underdosing may lead to operational upsets and potentially off-spec crude production. To overcome this challenge, innovative schemes (Smart Demulsifier Control & Wash Water Ratio Control) have been developed in-house. Smart Demulsifier Control optimizes the separation efficiency (or percentage of total produced water separated) of an upstream High Pressure Production Trap (HPPT or 3-Phase Separator) based on a dynamic target by adjusting the demulsifier injection rate and concentration in the wet crude. Simultaneously, wash water ratio control ensures that an adequate wash water rate is injected to satisfy salt-in-crude specifications. These control schemes eliminate the need for operators to determine the required dosage rate, thereby avoiding both overdosing and underdosing of demulsifier and wash water. The Smart Demulsifier Control (SDC) scheme controls demulsifier injection using two control layers. The first layer controls the Concentration of the Demulsifier in the Wet Crude so that demulsifier flow is automatically adjusted based on the Production Rate to achieve the set point concentration determined by the second layer of control. The second layer adjusts the demulsifier concentration to control the Separation Efficiency of the HPPT, or the amount of water separated in the HPPT vs. the Dehydrator, to achieve the Target Separation Efficiency Set Point determined by a site specific process model. In case of a dehydrator upset, another PID controller with more aggressive tuning will override the HPPT Separation PID Controller to set the required demulsifier concentration to mitigate the upset. Wash water ratio control scheme controls the flow of wash water to ensure that the salt-in-crude specification is met. A site specific target ratio is determined through a salt mass balance. These innovative controls have reduced desalting train upsets by 78% as the process related upsets are practically eliminated. This is achieved while optimizing the demulsifier dosage where 20-40% of demulsifier dosage reduction was realized, especially during the winter season. Moreover, savings of 20% wash water have been achieved throughout the utilization of thes
{"title":"Crude Oil Process Enhancement and Water Conservation Through Industrial Revolution Initiatives","authors":"Nasser A. Alhajri, R. White, M. A. Andreu","doi":"10.2118/195044-MS","DOIUrl":"https://doi.org/10.2118/195044-MS","url":null,"abstract":"\u0000 One of the primary functions of Saudi Aramco Gas-Oil Separation Plants (also known as GOSPs) is to separate emulsified water from the crude. The water is typically highly concentrated with salt, so crude desalting is required to meet the standard quality specifications. GOSPs are typically designed with standard Proportional-Integral-Derivative (PID) controllers to control demulsifier and wash water flow for injection into wet crude. Demulsifier and wash water injection rates are normally left to operator judgement. The challenge with manual adjustment of flowrate is the high risk of overdosing or underdosing as there are several variables that impact the required demulsifier and wash water rates. Overdosing will result in wastage of demulsifier and wash water and higher operating expenditures. Underdosing may lead to operational upsets and potentially off-spec crude production.\u0000 To overcome this challenge, innovative schemes (Smart Demulsifier Control & Wash Water Ratio Control) have been developed in-house. Smart Demulsifier Control optimizes the separation efficiency (or percentage of total produced water separated) of an upstream High Pressure Production Trap (HPPT or 3-Phase Separator) based on a dynamic target by adjusting the demulsifier injection rate and concentration in the wet crude. Simultaneously, wash water ratio control ensures that an adequate wash water rate is injected to satisfy salt-in-crude specifications. These control schemes eliminate the need for operators to determine the required dosage rate, thereby avoiding both overdosing and underdosing of demulsifier and wash water.\u0000 The Smart Demulsifier Control (SDC) scheme controls demulsifier injection using two control layers. The first layer controls the Concentration of the Demulsifier in the Wet Crude so that demulsifier flow is automatically adjusted based on the Production Rate to achieve the set point concentration determined by the second layer of control. The second layer adjusts the demulsifier concentration to control the Separation Efficiency of the HPPT, or the amount of water separated in the HPPT vs. the Dehydrator, to achieve the Target Separation Efficiency Set Point determined by a site specific process model. In case of a dehydrator upset, another PID controller with more aggressive tuning will override the HPPT Separation PID Controller to set the required demulsifier concentration to mitigate the upset.\u0000 Wash water ratio control scheme controls the flow of wash water to ensure that the salt-in-crude specification is met. A site specific target ratio is determined through a salt mass balance.\u0000 These innovative controls have reduced desalting train upsets by 78% as the process related upsets are practically eliminated. This is achieved while optimizing the demulsifier dosage where 20-40% of demulsifier dosage reduction was realized, especially during the winter season. Moreover, savings of 20% wash water have been achieved throughout the utilization of thes","PeriodicalId":11321,"journal":{"name":"Day 3 Wed, March 20, 2019","volume":"496 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77053581","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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