Day 2 Tue, August 03, 2021最新文献

{"title":"Optimisation of Offshore Structures Decommissioning – Cost Considerations","authors":"Emmanuel T. Eke, I. Iyalla, J. Andrawus, R. Prabhu","doi":"10.2118/207206-ms","DOIUrl":"https://doi.org/10.2118/207206-ms","url":null,"abstract":"\u0000 The petroleum industry is currently being faced with a growing number of ageing offshore platforms that are no longer in use and require to be decommissioned. Offshore decommissioning is a complex venture, and such projects are expected to cost the industry billions of dollars in the next two decades. Early knowledge of decommissioning cost is important to platform owners who bear the asset retirement obligation. However, obtaining the cost estimate for decommissioning an offshore platform is a challenging task that requires extensive structural and economic studies. This is further complicated by the existence of several decommissioning options such as complete and partial removal. In this paper, project costs for decommissioning 23 offshore platforms under three different scenarios are estimated using information from a publicly available source which only specified the costs of completely removing the platforms. A novel mathematical model for predicting the decommissioning cost for a platform based on its features is developed. The development included curve-fitting with the aid of generalised reduced gradient tool in Excel® Solver and a training dataset. The developed model predicted, with a very high degree of accuracy, platform decommissioning costs for four (4) different options under the Pacific Outer Continental Shelf conditions. Model performance was evaluated by calculating the Mean Absolute Percentage Error of predictions using a test dataset. This yielded a value of about 6%, implying a 94% chance of correctly predicting decommissioning cost.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75660358","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}

{"title":"Conceptual Field Development Plan for X Field","authors":"K. Ibrahim, P. Nzerem, Ayuba Salihu, I. Okafor, Oluwaseun Alonge, O. Ogolo","doi":"10.2118/207146-ms","DOIUrl":"https://doi.org/10.2118/207146-ms","url":null,"abstract":"\u0000 The development plan of the new oil field discovered in a remote offshore environment, Niger Delta, Nigeria was evaluated. As the oil in place is uncertain, a probabilistic approach was used to estimate the STOOIP using the low, mid, and high cases. The STOOIP for these cases were 95 MMSTB, 145 MMSTB and 300 MMSTB which are the potential amount of oil in the reservoir. Rock and fluid properties were determined using PVT sample and then matched to the Standing correlations with an RMS of 4.93%. The performance of the different well models were analyzed, and sensitivities were run to provide detailed information to reduce the uncertainties of the parameters. Furthermore, production forecast was done for the field for the different STOOIP using the predicted number of producer and injector wells. The timing of the wells was accurately allocated to provide information for the drillers to work on the wells. From the production forecast, the different STOOIP cases had a water cut ranging from 68-73% at the end of the 15-year field life. The recoverable oil estimate was accounted for 33.25 MMSTB for 95 MMSTB (low), 55.1 MMSTB for 145 MMSTB (mid) and 135 MMSTB for 300 MMSTB (high) at 35%, 38% and 45% recovery factor.\u0000 Based on the proposed development plan, the base model is recommended for further implementation as the recovery factor is 38% with an estimate of 55.1 MMSTB. The platform will have 6 producers and 2 injectors. The quantity of oil produced is estimated at 15000 stbo/day which will require a separator that has the capacity of hold a liquid rate of about 20000 stb/day. The developmental wells are subsequently increased to achieve a water cut of 90-95% with more recoverable oil within the 15-year field life. This developmental plan is also cost effective as drilling more wells means more capital expenditure.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"85 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80946860","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}

{"title":"Application of Machine Learniing For Reservoir Facies Classification in Port Field, Offshore Niger Delta","authors":"J. Asedegbega, Oladayo Ayinde, A. Nwakanma","doi":"10.2118/207163-ms","DOIUrl":"https://doi.org/10.2118/207163-ms","url":null,"abstract":"Several computer-aided techniques have been developed in recent past to improve interpretational accuracy of subsurface geology. This paradigm shift has provided tremendous success in variety of Machine Learning Application domains and help for better feasibility study in reservoir evaluation using multiple classification techniques. Facies classification is an essential subsurface exploration task as sedimentary facies reflect associated physical, chemical, and biological conditions that formation unit experienced during sedimentation activity. This study however, employed formation samples for facies classification using Machine Learning (ML) techniques and classified different facies from well logs in seven (7) wells of the PORT Field, Offshore Niger Delta. Six wells were concatenated during data preparation and trained using supervised ML algorithms before validating the models by blind testing on one well log to predict discrete facies groups. The analysis started with data preparation and examination where various features of the available well data were conditioned. For the model building and performance, support vector machine, random forest, decision tree, extra tree, neural network (multilayer preceptor), k-nearest neighbor and logistic regression model were built after dividing the data sets into training, test, and blind test well data. Results of metric score for the blind test well estimated for the various models using Jaccard index and F1-score indicated 0.73 and 0.82 for support vector machine, 0.38 and 0.54 for random forest, 0.78 and 0.83 for extra tree, 0.91 and 0.95 for k-nearest neighbor, 0.41 and 0.56 for decision tree, 0.63 and 0.74 for logistic regression, 0.55 and 0.68 for neural network, respectively. The efficiency of ML techniques for enhancing the prediction accuracy and decreasing the procedure time and their approach toward the data, makes it importantly desirable to recommend them in subsurface facies classification analysis.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82226608","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}

{"title":"3D Seismic Data Design, Acquisition and Interpretation of Kolmani Exploratory Field, Upper Benue Trough, Gongola Basin; Nigeria","authors":"U. Abdulkadir, Jamaluddeen Hashim, Ajay Kumar, Umar Yau, Akpam Simon, A. Dawaki","doi":"10.2118/207118-ms","DOIUrl":"https://doi.org/10.2118/207118-ms","url":null,"abstract":"\u0000 In an Oil and Gas field development plan, identifying appropriate reservoir location of a field and deciding the best design strategy as well as meeting the economic hydrocarbon viability are imperative for sustainability. 3-Dimensional seismic data have become a key tool used by geophysicists in the Oil and Gas industry to identify and understand subsurface reservoir deposits. In addition to providing excellent structural images, the dense sampling of a 3D survey can sometimes make it possible to map reservoir quality and the distribution of Oil and Gas.\u0000 Primarily, Seismic data sets were retrieved from the ongoing Kolmani exploratory work of upper Benue trough, bordering Gombe-Bauchi communities of Nigeria and Simulation study from improve design was conducted using PETREL and SURFER software's to obtain numerous coordinates from the source and receiver lines respectively and subsequent formation of strategic-designs that shows different arrangements of the prospect area, an interpretation of the acquired data sets that indicates the reservoir location appropriately and probable onset of drilling spot. The well to seismic was also merged using synthetic seismogram that shows the location of reservoir (s) from the seismic data obtained and four different wells with anticipated depths respectively. The overall aim of the whole design and simulation studies is to aid petroleum Geologist and Geophysicists avoids common pit falls by reducing dry holes and increasing the overall number of productive wells prior to actual commencement of drilling in this prospect area and elsewhere.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"07 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76234314","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}

{"title":"Adulteration Detection of Petroleum Products at Point of Sale POS Terminals","authors":"O. Ejofodomi, G. Ofualagba, D. Onyishi","doi":"10.2118/207101-ms","DOIUrl":"https://doi.org/10.2118/207101-ms","url":null,"abstract":"\u0000 In the Oil and Gas Industry, price disparity between Premium Motor Spirit (PMS), Automotive Gas Oil (AGO), and Dual Purpose Kerosene (DPK), often leads to adulteration of these petroleum products by marketers for monetary gains. Adulteration is the illegal introduction of a foreign undesirable substance to a substrate which affects the quality of the substrate. Adulteration of petroleum products are difficult to detect at Point of Sale (POS) terminals. Current methods for adulteration detection are time-consuming, require specialized equipment and experienced technicians to operate them, and cannot be used at POS terminals. Gaseous Vapor Technique (GVE) is an innovative adulteration detection technique that can be employed at POS terminals and the PePVEAT device utilized in this study is the first portable electronic device that performs GVE on petroleum products. GVE testing was performed on pure 1 L samples of PMS, AGO, and DPK obtained from the Nigerian National Petroleum Corporation (NNPC) using PePVEAT. The results obtained from GVE analysis of AGO, PMS, and DPK showed that the three petroleum products exhibited unique and varying chemical characteristics during GVE.\u0000 AGO gives off its peak emissions between 10-20 seconds from test onset, DPK gives off its peak emissions between 10-30 seconds from test onset, and PMS gives off its peak emissions between 50-70 seconds from test onset. AGO emits 17.52-46.58 ppm of methane, 5.35-11.93 ppm of LPG, 35.51-84.6 ppm of butane, and 10.38-69.86 ppm of toluene. PMS emits 92,063.67-152,168.18 ppm of methane, 301.035-573.61 ppm of LPG, 2210.89-3424.94 ppm of butane, and 1983.02-7187.29 ppm of toluene. DPK emits 27.13-62.14 ppm of methane, 20.2-74.1 ppm of LPG, 120.41-1635.85 ppm of butane, and 1159.75- 1633.09 ppm of toluene.\u0000 These variations in timing and concentrations of emissions shows that GVE can be utilized to detect and distinguish between AGO, PMS and DPK. The results obtained from GVE analysis of AGO, PMS, and DPK showed that Since PMS, AGO and DPK, each have unique chemical emissions during GVE, as was demonstrated in this paper, it is possible that GVE can be utilized to detect the adulterations of PMS with AGO and the adulteration of AGO with DPK. Future work involves investigating the ability of GVE to detect AGO-adulterated PMS, DPK-adulterated AGO, DPK-adulterated PMS, AGO-adulterated DPK,and PMS-adulterated DPK. The degree and percentage of adulteration that can be detected using the GVE technique will also be examined.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88218011","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}

{"title":"Unconventional Method of Estimating Oilfield Reserve Initially in Place Using Decline Trends Analyses Techniques","authors":"Celestine A. Udie, F. Faithpraise, Agnes Anuka","doi":"10.2118/207109-ms","DOIUrl":"https://doi.org/10.2118/207109-ms","url":null,"abstract":"\u0000 Methods to estimate reserves, recovery factor and time are highlighted using uconventional method, to reduce the challenges in an oilfield development. General Information about reserves production estimation using long and short production data is collated. The collated data are plotted against time to build production decline curves. The curves are used to estimate the decline rate trends and constants. The decline constant is then used to predict reserves cumulative recovery. The rate trend is extrapolated to abandonment for estimation of reserves initially in place, recovery factor and the correspondent time. The reserves values are compared with field values for accuracy. It was observed that the result using data from long time production history accuracy was 99.98% while evaluation models built with data from short production history accuracy was 98.64%. The models are then adopted after validation. The validated curves are used to build the governing models which are finally used in estimating cumulative reserves recovery and initially in place. It is concluded that accurate reserves, recovery factor and time estimation challenges can be achieved/matched up using rate decline trend techniques.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88334053","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}

{"title":"Innovative Use of Injectivity Tests as Interference Tests during Field Development: The EGINA Experience","authors":"O. Mogbo, A. Atewologun","doi":"10.2118/207181-ms","DOIUrl":"https://doi.org/10.2118/207181-ms","url":null,"abstract":"\u0000 This paper presents the innovative use of interference tests in the assessment of reservoir connectivity and the field oil production rate during the development phase and prior to the first oil of the EGINA field, which is located in a water depth of 1600 m, deep offshore Niger Delta. The interference test campaign involved 26 pre-first oil wells (13 oil producers and 13 water injectors) to assess and subsequently mitigate reservoir connectivity uncertainties arising from the numerous faults and between the different channels within the complexes. The results proved valuable in confirming or otherwise reservoir connectivity, field oil production rate and the number of wells required at first oil to achieve the production plateau.\u0000 The tests were designed using the analytical method (PIE software) and the reservoir simulation models enabling to establish the cumulative water injection required, the injection duration and the time a response is expected at the observers. These all had impacts on the planning, OIMR vessel requirements and selection of permanent downhole gauges for the wells.\u0000 In addition, the tests were performed with the water injectors as pulsers and the oil producers as observers allowing to avoid and the associated environmental impact. Ten interference tests were realized compared to four planned in the FDP partly due to the use of the more cost effective OIMR vessel in addition to the rig.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82719703","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}

{"title":"The Role of Digitalization in Decarbonizing the Oil and Gas Industry","authors":"Peace Bello","doi":"10.2118/207125-ms","DOIUrl":"https://doi.org/10.2118/207125-ms","url":null,"abstract":"\u0000 As the Oil & Gas industry journeys towards net zero carbon emissions, a lot needs to be done, one of which is the adoption of digital transformation across companies. Decarbonization requires a transformational shift in the way companies operate, how they source, use, consume and think about energy and feedstocks. If the Oil & Gas sector will continue to exist, it must carry out its activities in the safest possible way and digitalizing it will help in achieving this.\u0000 A survey by Newsweek shows that areas where transformative technologies are having the biggest impact are production-related, operations and maintenance, enhanced recovery, fracking/tight reservoirs, and exploitation at greater depths. Luis Abril of Minsait opined that digital technology enables companies to extract more value from data, using new platforms to share data with the entire organization, suppliers, contractors, and partners. The real-time visualization of data helps optimize decision making.\u0000 Big data can be analyzed to find answers to questions such as: What piece of equipment is showing signs of wear and should be replaced? What sort of predictive maintenance can be leveraged? What is the most effective fracking approach for this well? AI helps to reduce routine flaring, employ methane capture, optimize production and reservoir management using digital tools such as IoT sensors, digital twins, and virtual reality to model scenarios, monitor operations, track emissions, energy usage and proactively maintain equipment, produce lower-emission products by moving from one hydrocarbon to another (e.g., from coal to natural gas) or creating another product (such as biofuels or syngas).\u0000 Transformative technologies, particularly IoT, mobility and cloud applications are going to have a profound effect on the future of the oil and gas sector. Investment in these technologies cost a lot which might be difficult for private companies, but it is worth the money in the long run.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87397317","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}

{"title":"Development of a Real–Time Petroleum Products Aduteration Detector","authors":"Olabisi Olotu, S. Isehunwa, B. Asiru, Z. Elakhame","doi":"10.2118/207127-ms","DOIUrl":"https://doi.org/10.2118/207127-ms","url":null,"abstract":"\u0000 Adulteration of petroleum products with the resultant safety, health, environmental and economic impact is a challenge in Nigeria and many developing countries. While the commonly used techniques by regulatory agencies and some end-users for quality assurance of petroleum products are time-consuming and expensive. This study was therefore designed to develop a device for real-time detection of petroleum products adulteration.\u0000 Samples of petrol, diesel and kerosene were collected; samples of water, naphtha, alcohol, pure and used lubricating oil, and High Pour Fuel Oil (HPFO) were collected and used as liquid contaminants while saw dust, ash and fine sand were used as solid particulates. At temperatures between 23-28°C (1°C interval), binary mixtures were prepared using the pure products with liquid contaminants (95:5, ..,5: 95 V/V) and with particulates (0, 2, 4, 6, 8,10 g). New mixing rules were developed for the SG and IFT of the binary liquid mixtures and compared with Kay mixing rule. Developed mathematical models of the physical-chemical properties were used to simulate a meter designed and constructed around a microcontroller with multiple input/output pins and a load cell sensor.\u0000 The SG and IFT of the pure liquid and solid binary mixtures ranged from 0.810 to 1.020, 25.5 to 47.2 dynes/cm and 0.820 to 1.080 and 26.3 and 50.2 dynes/cm respectively. For products contaminated with solid particulates, SG varied between 0.860 and 0.990. The new mixing rule gave coefficient of 0.84 and 27.8 for SG and IFT compared with 0.83 and 25.6 of Kay's model. Adulteration of products was detected at 20-30% by volume and 10-20% by mass of contamination, and displayed RED for adulterated samples, GREEN for pure samples and numerical values of SG in digital form which were within ±0.01 % of actual measurements.\u0000 A device for real-time detection of adulteration in petroleum products was developed which can be adapted to real-time evaluation of similar binary mixtures.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75352954","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}

{"title":"Application of Machine Learning in Predicting Crude Oil Production Volume","authors":"Okechukwu Innocent","doi":"10.2118/207079-ms","DOIUrl":"https://doi.org/10.2118/207079-ms","url":null,"abstract":"\u0000 The production of oil is of great and immense significance as a source of energy worldwide. The major factors affecting the production volume of oil is classified into two groups namely the geological and the human factor. Each group comprises of factors affecting oilfield production volume. The challenge in this project is to find the variable for the crude oil production volume in an oilfield because there are numerous factors affecting the crude oil production volume in an oilfield. The objective of this paper is to provide a more accurate and efficient solution on how to predict the oil production volume.\u0000 Furthermore, Machine Learning algorithm called Multiple Linear Regression was developed using Python programming Language to predict the production volume of oil in an oilfield. The model was developed and fitted to train and test the factors that affect and influence the oil production volume. After a several studies have been made, the affecting factors were provided from the oilfield which would be trained and tested in order to model the relationship between predictor variable and response variable which are the significant affecting factors and the oil production volume respectively. The predictor variables are the startup number of wells, the recovery percent of previous year, the injected water volume of previous year and the oil moisture content of previous year. The predictor variable is the oil production volume.\u0000 Moreover, the model was found to possess greater utility in predicting the production volume of oil as it yielded an oil production volume output with an accuracy of 98 percent. The relationship between oil production volume and the affecting factors was observed and drawn to a perfect conclusion.\u0000 This model can be of immense value in the oil and gas industry if implemented because of its ability to predict oilfield output more accurately. It is an invaluable and very efficient model for the oilfield manager and oil production manager.","PeriodicalId":10899,"journal":{"name":"Day 2 Tue, August 03, 2021","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73920673","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}