Dashti, Qasem (Kuwait Oil Company) | Moosa, M.H. (Schlumberger) | Erdman, M. (Shell Kuwait Exploration & Production) | Jensen, P. (Shell Kuwait Exploration & Production) | Olusegun, Kolawole (Kuwait Oil Company) | Al-Qadeeri, Bashar (Kuwait Oil Company) | Dhote, Prashant (Kuwait Oil Company)
Kuwait Oil Company (KOC) is going through many new challenging projects that aim to increase its hydrocarbons production capacity by 70%. The North Kuwait Jurassic Gas Fields project is one of the key projects with unique challenges from the subsurface complex and challenging characteristics of deep reservoirs, high pressure high temperature (HPHT), high in H2S and CO2 concentration-to the design, construction and operating of surface facilities. The Gas Field Development (GFD) group was established in 2007 to manage and accomplish KOC’s desired objectives from the NKJ Gas Fields project. The new group had to recruit manpower and build the required technical skills to address the unique challenges. End of 2010, KOC-GFD entered into an Enhanced Technical Service Agreement (ETSA) with Shell in order to benefit from the International Oil Company (IOC) expertise. One of ETSA objectives is to develop local KOC staff through Knowledge Transfer, whereas challenge was more than 60% of the total GFD population were juniors, i.e. less than 4 years of experience.
The need to fast track the development of the new recruited staff was identified by the management as a critical key element to overcome the project complex challenges. The development of a new approach for staff development using the best of both worlds’ i.e. building on KOC’s training programs and supplementing with Shell Jurassic ETSA Knowledge Transfer resulted in the creation of the Technical Competence Ladder, TCL, framework for all GFD staff in 2017. This technical paper will describe how the Jurassic ETSA Knowledge Transfer progressed over the course of the contract; connected with GFD business objectives; used key methodologies for successful application in the day-to-day activities; promoted a performance-based learning environment; used critical resources with clear accountabilities; was monitored and measured continually; Implemented with structured approached.
progressed over the course of the contract;
connected with GFD business objectives;
used key methodologies for successful application in the day-to-day activities;
promoted a performance-based learning environment;
used critical resources with clear accountabilities;
was monitored and measured continually;
Implemented with structured approached.
The results include the development of Structure and detailed competence skills development program for main subsurface disciplines like: Reservoir Engineering, Petrophysics, Geosciences, & Petroleum Engineering. Each main discipline includes number of specialization and focused sub-programs. The TCL program was implemented, and the Knowledge Transfer are proven. The progress of junior staff competences has been tracked and measured over the years; the creation of motivated and competent workforce has resulted in improved performance and increased team productivity. The overall results reduced ‘existing’ competency gaps within the company, enhanced communication between junior and senior staff, improved staff confidence and work performance. Key examples of success will illustrate the points covered in the technical paper.
Rajput, Sanjeev (Petronas Carigali Sdn Bhd) | Bt Abdullah, Irmawaty (Petronas Carigali Sdn Bhd) | Roy, Amit (Petronas Carigali Sdn Bhd) | B. Khalid, Aizuddin (Petronas Carigali Sdn Bhd) | Onn, Camellia (Petronas Carigali Sdn Bhd) | Khalil, Ashraf (Petronas Carigali Sdn Bhd)
Low electrical resistivity and low contrast reservoirs (LRLC) pay zones are composed of thinly-bedded laminated layers containing hydrocarbon accumulations surrounded by non-reservoir layers indicating lack of resistivity contrast. These pay zones are difficult to be distinguished at seismic and log scale due to lower vertical and lateral resolution. Traditionally, deep-resistivity logs in LRLC zones read 0.5 to 5 ohm-m. Low contrast pay zone occurs mainly when the formation waters are fresh or having low salinity resulting in a very little resistivity contrast between oil and water zones. Major challenges imposed in LRLC reservoirs include identification, characterization, and evaluation of the hydrocarbon interval, which is usually masked by the lack of resistivity contrast between the hydrocarbon and water zones. The identification and characterization of the lowdown on resistivity pay is essential for the re-development of mature assets for improved oil recovery. This paper deals with the characterization of low resistivity hydrocarbon-bearing thinly-bedded reservoirs from a brownfield.
To unlock the hidden potential of LRLC pay sands in the offshore Sarawak Malaysia, the effective integration of subsurface disciplines including petrophysics, geology and quantitative derivatives from the seismic analysis is vital. This study covers the geological perspective of low contrast reservoirs from an offshore oil field deposited in lower coastal plain settings located within offshore Sarawak Malaysia. An improved understanding of the geological, petrophysical and geophysical parameters was achieved by adopting a holistic and multidisciplinary approach. This includes the integration of core, logs, rock physics modeled parameters, stratigraphic, depositional and lithofacies information along with stochastic inversion derivatives. Acoustic Impedance shows the facies changes in broader terms between producing and non-producing zone.
The paper quantifies rock physics parameter uncertainties for LRLC pay zones and establishes a framework for LRLC reservoir characterization. Stochastic inversion derived P-Impedance and Vp/Vs ratio are used to predict fluid and facies probabilities (
Identified LRLC reservoirs proved to be of commercial-quality and increased oil production to the extent of several hundred thousands of barrels over the years and currently producing. Rock physics modeled parameters including AI and Vp/Vs are sensitive to LRLC pay zones and their effective integration with image logs, lithofacies, and seismic inversion lead to reduce uncertainties in infill drilling programs. Geological understanding of the possibility of LRLC occurrences is required to assess oil and gas bypassed oil. Detailed geological features are clearly resolved in high-definition image logs. Low resistivity pay zones present in the main reservoir intervals can be identified by integrating the information from low gamma ray, low impedance, and low resistivity zones collectively. The results of this study show the value of integrated approaches and improvements in reservoir description from stochastic inversion into reservoir models.
This paper introduces a new core-analysis work flow for determining resistivity index (RI), formation factor (FF), and other petrophysical properties directly from an as-received (AR) set of core samples. In this paper, the authors discuss the characterization process for GR tools and how they behave in boreholes different from the one used in the University of Houston (UH) GR characterization pit. This paper discusses a study undertaken to gain better understanding of nuclear magnetic resonance (NMR) characteristics of volcanic reservoirs with different lithologies. Formation evaluation drew special attention at the 2019 International Petroleum Technology Conference Education Week in Beijing, 24–28 March 2019. The student team that worked on Integrated Formation Evaluation for Resources Exploration and Reservoir Delineation won the first-place award. The first subsea multiphase boosting system was installed in 1994. Since then, it has grown into a technology with a global track record. A new enabling technology known as electrically heat-traced flowline (EHTF) will be used to enable system startup and shutdown and to maintain production fluids outside of the hydrate envelope during steady-state operation. This study incorporates previous learnings, as well as globally collected data, to develop a strategy that can be used to help implement an industry-specific mental health program. The value of hidden-danger data stored in text can be revealed through an approach that can help sort and interpret information in an ordered way not used previously in safety management. This paper highlights the results of a test campaign for a tool designed to predict the short-term trends of energy-efficiency indices and optimal management of a production plant. This paper presents the recent expansion of UNFC guidance to cover social and environmental effects and the further transformation of the system to make it a valuable tool in resource management for governments and businesses.
Anchored by the Khaleesi-Mormont and Samurai fields, the King’s Quay FPS will receive and process up to 80,000 B/D of crude oil. UK operator Trident Energy is entering Brazil while Australian firm Karoon Energy is expanding its position in the country. Both will try to boost output from already-producing assets. The world’s largest oil producer has awarded $18 billion in engineering, procurement, and construction contracts as part of its Marjan and Berri expansion projects. The Norwegian operator will increase its ownership of the giant Johan Sverdrup field before the field commences oil production this November.
This page pulls together technology-focused articles from various departments within JPT. This paper introduces a new core-analysis work flow for determining resistivity index (RI), formation factor (FF), and other petrophysical properties directly from an as-received (AR) set of core samples. In this paper, the authors discuss the characterization process for GR tools and how they behave in boreholes different from the one used in the University of Houston (UH) GR characterization pit. This paper discusses a study undertaken to gain better understanding of nuclear magnetic resonance (NMR) characteristics of volcanic reservoirs with different lithologies. Formation evaluation drew special attention at the 2019 International Petroleum Technology Conference Education Week in Beijing, 24–28 March 2019. The student team that worked on Integrated Formation Evaluation for Resources Exploration and Reservoir Delineation won the first-place award. The first subsea multiphase boosting system was installed in 1994.
You, Junyu (Petoleum Recovery Research Center) | Ampomah, William (Petoleum Recovery Research Center) | Kutsienyo, Eusebius Junior (Petoleum Recovery Research Center) | Sun, Qian (Petoleum Recovery Research Center) | Balch, Robert Scott (Petoleum Recovery Research Center) | Aggrey, Wilberforce Nkrumah (KNUST) | Cather, Martha (Petoleum Recovery Research Center)
This paper presents an optimization methodology on field-scale numerical compositional simulations of CO2 storage and production performance in the Pennsylvanian Upper Morrow sandstone reservoir in the Farnsworth Unit (FWU), Ochiltree County, Texas. This work develops an improved framework that combines hybridized machine learning algorithms for reduced order modeling and optimization techniques to co-optimize field performance and CO2 storage.
The model's framework incorporates geological, geophysical, and engineering data. We calibrated the model with the performance history of an active CO2 flood data to attain a successful history matched model. Uncertain parameters such as reservoir rock properties and relative permeability exponents were adjusted to incorporate potential changes in wettability in our history matched model.
To optimize the objective function which incorporates parameters such as oil recovery factor, CO2 storage and net present value, a proxy model was generated with hybridized multi-layer and radial basis function (RBF) Neural Network methods. To obtain a reliable and robust proxy, the proxy underwent a series of training and calibration runs, an iterative process, until the proxy model reached the specified validation criteria. Once an accepted proxy was realized, hybrid evolutionary and machine learning optimization algorithms were utilized to attain an optimum solution for pre-defined objective function. The uncertain variables and/or control variables used for the optimization study included, gas oil ratio, water alternating gas (WAG) cycle, production rates, bottom hole pressure of producers and injectors. CO2 purchased volume, and recycled gas volume in addition to placement of new infill wells were also considered in the modelling process.
The results from the sensitivity analysis reflect impacts of the control variables on the optimum results. The predictive study suggests that it is possible to develop a robust machine learning optimization algorithm that is reliable for optimizing a developmental strategy to maximize both oil production and storage of CO2 in aqueous-gaseous-mineral phases within the FWU.
The Programme Committee of the 2019 SPE Russian Petroleum Technology Conference invites you to submit a paper proposal and contribute to this event. The paper proposal categories below are used to direct the paper proposals to the appropriate subject matter experts for evaluation. Please choose one or several categories and submit your paper.
Gupta, Anish (PETRONAS) | Narayanan, Puveneshwari (PETRONAS) | Trjangganung, Kukuh (PETRONAS) | Mohd Jeffry, Suzanna Juyanty (PETRONAS) | Tan, Boon Choon (PETRONAS) | Awang, M Rais Saufuan (PETRONAS) | Badawy, Khaled (PETRONAS) | Yip, Pui Mun (PETRONAS)
A matrix stimulation candidate screening workflow was developed with the objective to reduce the time and effort in identifying under-performing wells. The workflow was initially tested manually for few fields followed by inclusion in Integrated Operation for an automated screening of wells with suspected formation damage. Analysis done in three fields for stimulation candidate selection will be displayed with actual statistics.
The main aim of the work was to digitalize the selection of non-performing candidates rather than manually looking into performance of each well. A concept of Formation Damage Indicator (FDI) was combined with Heterogeneity Index (HI) of the formations to screen out the candidates. Separate database sets of Reservoir engineering, Petrophysicist and Production was integrated with suitable programming algorithms to come up with first set of screened wells evaluating well production performances, FDI and HI trends up to over the last 30 years. The shortlisted candidates were further screened on the basis of practical approach such as gas lift optimization, production trending, OWC-GOC contacts, well integrity and well history to come up with second round of screened candidates. The final candidates were analyzed further using nodal analysis models for skin evaluation and expected gain to come up with type of formation damage and expected remedial solution.
For fields A and D with a total of 210 strings each, the initial FDI and HI screening resulted in 70 and 120 strings being shortlisted, respectively. This was followed by a second round of screening with 25 and 35 strings being further shortlisted as stimulation candidates, respectively. Nodal analysis models indicated presence of high skin in 90% of the selected wells indicating a very good efficiency and function-test of the workflow. In addition to selection of the candidates, the identification of formation damage type was compiled on an asset-wise basis rather than field basis which helped in more efficient planning of remedial treatments using a multiple well campaign approach to optimize huge amount of cost. The entire screening process was done in one month which was earlier a herculean task of almost one year and much more man-hours. With effective manual testing of the workflow in two major fields, workflow was included in Integrated Operations for future automation to conduct the same task in minutes rather than months.
With this digitalized unique workflow, the selection of under-performing wells due to formation damage is now a one click exercise and a dynamic data. This workflow can be easily operated by any engineer to increase their operational efficiency for flow assurance issues saving tons of cost and time.
Yuping, Sun (RIPED,Petrochina) | Chunxiao, Guan (RIPED,Petrochina) | Jingping, Zhang (RIPED,Petrochina) | Qiaojing, Li (RIPED,Petrochina) | Jialiang, Lu (RIPED,Petrochina) | Hongjun, Tang (RIPED,Petrochina) | Weijun, Shen (Institute of Mechanics of Chinese Academy of Sciences) | Haibo, Li (RIPED,Petrochina) | Hewen, Zhang (RIPED,Petrochina)
Large gas fields play an important role in natural gas industry. Recovery rate, plateau duration, recovery at the end of plateau, decline rate and recovery factor are the key development indexes for dynamic performance analysis and development planning. Scientific prediction for those indexes can support gas development planning strongly.
Through mining statistical analysis of 150 large gas fields and numerical simulation analysis, 23 objective influencing factors which affect the development effect are studied. Gas fields are classified according to the main influencing factors, and then the distribution of development indexes are summarized. Finally, a series of prediction methods for key development indexes are established.
Based on the above work, it is found that matrix permeability, drive types, reservoir architecture and fluid type are the most sensitive factors among them. According to the most sensitive factors, gas fields should be divided into 4 categories, and 13 subcategories and the distributions rules of development indexes of all categories are presented. Then new prediction methods for development indexes are established, including linear empirical formula method, similarity analogy prediction method based on Euclidean theorems, and probabilistic values method. In this process, according to the characteristics of influencing factors, logarithmic and piecewise function methods are used for dimensionless treatment, and the prediction accuracy of the methods is improved. Finally, the expert system software is developed which can automatically predict the key development indexes. The prediction accuracy is over 80% which can satisfy the requirement of strategic planning.
The new methods have the characteristics of multiple methods, applicable to multiple gas field types and predicting multiple development indexes. Those methods can be applied to predict the development indexes of new fields and evaluate the development effects of matured gas fields in batch.