By International Petroleum Technology Conference (IPTC) Monday, 25 March 0900-1600 hours Instructors: Olivier Dubrule and Lukas Mosser, Imperial College London Deep Learning (DL) is already bringing game-changing applications to the petroleum industry, and this is certainly the beginning of an enduring trend. Many petroleum engineers and geoscientists are interested to know more about DL but are not sure where to start. This one-day course aims to provide this introduction. The first half of the course presents the formalism of Logistic Regression, Neural Networks and Convolutional Neural Networks and some of their applications. Much of the standard terminology used in DL applications is also presented. In the afternoon, the online environment associated with DL is discussed, from Python libraries to software repositories, including useful websites and big datasets. The last part of the course is spent discussing the most promising subsurface applications of DL.
The primary aim of this study was to design a guideline in the form of a flow chart that can potentially be used in practice when optimal sand control methods and implementation of completion techniques are considered in sand producing wells. The flow chart could also serve as a tool during the decision-making process where sand control is deemed necessary. Distinct reservoir data and key parameters were incorporated into the designing of the flow chart. This paper predominantly focused on two of the most common techniques used currently in practice; sand screens and gravel pack.
A retrospective analytical review was carried out. A systematic search was undertaken to analyze sand control methodologies used in recent studies to ascertain key considerations undertaken when choosing gravel pack and/or sand screen. Studies selected were based on a predetermined set of inclusion and exclusion criteria. Recurrent pattern that existed when choice of a specific technique was identified; a list of criteria that was considered when selecting gravel pack and/or sand screen techniques was developed. Information and data obtained were then eventually integrated in stages into designing a concise flowchart.
List of criteria developed when contemplating sand control and completion methods were as follows, 1. Determine rock mechanics, 2. Study individual reservoir conditions, 3. Note lithological changes, 4. Obtain well data, 5. Characterize formation sand, 6. Select gravel size, 7. Select screen and size, 8. Select completion method and 9. Evaluate the potential cost and economical outcome. The development of flow chart then began with the categorisation of key information into four significant stages as follows, sand prediction, sand analysis, sand control and completion method. Looking at the step 1 to 9 of the list of criteria and the four phases of the flow chart together, a more structured and integrated thought process took place. When the sand analysis stage was referred to, criteria 1 to 5 were determined simultaneously. Then, the sand prediction stage was referred to in the flow chart where upon obtaining the sand production rate it could be determined if the sand produced was low/manageable. A choice to live with the sand produced is made if so. If the rate was high, then the rest of criteria 6 to 9 as per the list above were determined and advancement is made to the next stage flow chart according to the sections. Both, the criteria list and flow chart can be used in parallel as guidance when implementing respective techniques in an individual sand-producing well.
Intelligent Reservoir Management and Monitoring has played a key role in the pursuit of improving the hydrocarbon recovery and reducing the development expenditure in the challenging multi-stacked compartmentalized fields which have proved to be perplexing in a number of ways which include preventing or delaying water breakthrough, extenuating wellbore instability, sand production etc. Reservoir-management and monitoring options have been greatly improved in recent decade by smart completions comprising of downhole monitoring and control equipments like permanent down-hole gauges to have "eyes" into the reservoir and to monitor performance for each zone; dynamic active flow control valves, which aid in equalization of the reservoir inflow into the wellbore; and the SCADA system which enables the real time monitoring and control of the downhole and surface equipment remotely from the control room.
Crampin, Tom (Brunei Shell Petroleum Co. Sdn. Bhd.) | Gligorijevic, Aleksandar (Geoservices) | Clarke, Ed (Shell) | Burgess, Jamie (Brunei Shell Petroleum Co. Sdn. Bhd.) | Chung, Shao-Jung (Brunei Shell Petroleum)
Downhole determination of hydrocarbon phase is a significant subsurface challenge in many highly depleted fields. Reservoir production results in fluid compositional changes and variable hydrocarbon saturation distributions. Standard petrophysical techniques such as analysis of density and neutron porosity logs can give misleading results under such conditions. Most commonly, oil reservoirs can display a neutron-density response indicative of gas. There is significant business impact in error of hydrocarbon phase determination. Mistakes can lead to poor completion decisions, incorrect reserves estimation and suboptimal well and reservoir management.
The fluid phase uncertainty resulting from interpretation of standard Logging While Drilling (LWD) datasets can be unacceptably high. Additional tools or techniques are therefore required. Downhole fluid sampling is one such technique. It is routinely and successfully acquired in exploration and appraisal wells and gives robust fluid phase determination. However, it is not economically feasible for frequent acquisition for in-fill production wells where low cost LWD acquisition is the norm. In addition, overbalanced wells drilled through highly depleted reservoirs lead to acquisition risk in stationary openhole logging techniques. Advanced Mud Gas logging (AMG) is an established tool for delivering real-time quantitative fluid composition in exploration, appraisal and early production wells. However, successful applications in highly depleted fields have not been published as AMG analysis can be complicated by compositional changes. In this paper we present a case study calibration of AMG with downhole fluid samples resulting in a robust, cost effective and safe tool for improved hydrocarbon phase determination in depleted reservoirs.
Many techniques are used to determine hydrocarbon phase but all of them can be impacted by production related changes to reservoir fluids. The neutron-density "cross-over?? is the most common gas identification tool (Figure 1). It results from an anomalously low neutron porosity reading in gas, due to low hydrogen index (HI), and an anomalously high density porosity reading, due to low fluid density. A second traditional technique is the neutron near count to far count ratio. The near detector reads largely in the near wellbore invaded zone where high mud filtrate saturation results in a high HI and a relatively low count rate when overlain with the far detector, which reads deeper into the formation, past the invaded zone, resulting in a relatively high count rate if gas is present.
Legarth, B. (Brunei Shell Petr. Sdn Bhd) | Dustin, S. (Brunei Shell Petr. Sdn Bhd) | Montero, J. (Brunei Shell Petr. Sdn Bhd) | Walker, J. (Schlumberger ) | Mulligan, R. (Schlumberger) | Maeso, C. (Schlumberger )
Copyright 2013, SPE/IADC Drilling Conference and Exhibition This paper was prepared for presentation at the SPE/IADC Drilling Conference and Exhibition held in Amsterdam, The Netherlands, 5-7 March 2013. This paper was selected for presentation by an SPE/IADC program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers or the International Association of Drilling Contractors and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers or the International Association of Drilling Contractors, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers or the International Association of Drilling Contractors is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE/IADC copyright. Abstract Some of the oil fields offshore Brunei are characterized by complex reservoir geology. This requires the drilling of highcomplexity, tortuous 3D horizontal wells referred to as "snake wells" for optimal reservoir drainage. These wells deliver an ultimate recovery equivalent to multiple horizontal wells drilled in the same structure. This development concept was chosen as the most beneficial with the business value drivers for the Selangkir Iron Duke (SKID) project. Over a period of several years, drilling performance had improved but plateaued and still contained hours of nonproductive time (NPT), including hole conditioning wiper trips, rough drilling causing bottomhole assembly (BHA) failures due to vibrations, troublesome trips, and even lost production due to stuck-pipe incidents.
In the quest of improving the hydrocarbon recovery and reducing the development cost in the challenging multi-stacked compartmentalized fields as well as oil rim reservoirs in Malaysia, well type and completion design was found to play a major role. Intelligent well design and completions, namely multi-lateral, selective and controlled injection and depletion, dynamic active flow control valves and down-hole pressure/temperature/composition monitoring have been identified as an essential component in the enhancement of the development strategy. Smart/intelligent completions have the ability to prevent/delay water or gas breakthrough, increase the productivity index and also to properly control drawdown to mitigate wellbore instability, sand failure and conformance issue. Active flow control valves also allow for fewer wells to be drilled by enabling efficient commingled injection and production wells. Moreover, with down-hole monitoring and surveillances, unplanned and challenging work-overs can be avoided, further reducing operating costs. The study also focuses on well architecture that entails well type selection, well reservoir penetration, well inclination and orientation, well completion simplification, well placement and well-count optimization.
In this paper, examples of mature complex multi-stacked and compartmentalized reservoirs with very thin to thick oil columns have been studied for improving the development and exploitation strategies through application of intelligent well type and optimum completion design and engineering. The suggested technology tool box including the applied workflow, guideline, procedures and standards with the field examples and desired results are to be presented and discussed. The study will cover from the assessment to the implementation and execution as well as the modeling methodology of the smart well technology on the selected fields in Malaysia.
Malaysia features a plethora of reservoirs that are; multi-stacked, compartmentalized and/or marginal oil rims (wedged in-between a gas-cap and an aquifer). Additionally these reservoirs are complex in structure with relatively high levels of reservoir heterogeneity. By their nature, these reservoirs present a challenge to be commercially productive as well as viable for active reservoir management.
An intelligent well design and completion feasibility study was commissioned to critically analyze the technical and commercial impact of application of this technology to specific fields in Malaysia. A pilot selection of 4 fields with reservoir characteristics representative of Malaysia's wide range of diversified oil and gas fields were chosen as a basis for this case study. The purpose of the study was to help qualify and quantify the field specific benefits of incorporating intelligent completions as part of field development plans. The results of this study would form part of an inventory resource to be utilized for realizing a field's optimal value via efficient development of mature, complex, multi-stacked, compartmentalized and thin-oil rim reservoirs.
Brunei Shell Petroleum (BSP) first started completing Smart Wells in 1999, trialing standalone technologies such as permanent downhole gauges and inflow control valves in individual wells. Once these were seen as successful, the technology was used extensively on a single platform. This was later extended to application in a whole field, taking advantage of refinements such as variable downhole control valves and multiphase flow metering.
Learning from the successes of other oil producing fields such as Champion West and Bugan, Seria North Flank was planned and designed as a fully Smart field. Seria North Flank would be the first field to fully integrate Smart technology with Smart field processes, improving the efficiency of Well and Reservoir Management activities and accelerating reservoir understanding in order to reduce uncertainties for future development. This resulted in the development of over 120 million barrels of oil, with improved Unit Technical Costs compared to an offshore development.
Building Smart capabilities
Brunei Shell Petroleum (BSP) initially trialed the individual elements of Smart technology in standalone wells from 1999. Downhole gauges and fibre optics (for Distributed Temperature Sensing, or DTS) were run on different wells, mainly to trial the technology and study reservoir inflow profiles. Several key findings from these wells formed the basis for the requirements of surface operated inflow control valves (ICV). The main one of these was that contribution from long horizontals tended to be negligible from the toe of the well (furthest from the stinger section).
The Champion West field was selected to further develop Smart technology. In order to manage and develop appropriate solutions for the network infrastructure, a dedicated IT team was created to support to real time data management. A small team within the operations discipline was also formed to help manage the interfaces with the existing offshore network infrastructure.
Initial completion designs incorporated one ICV above a ball valve and a dual gauge for multizone wells. At this stage, only monitoring was applied remotely with the downhole and surface gauge data transmitted to the main production facility and control requiring human intervention at the platform location. These wells showed the time benefits of have surface operated capabilities to monitor well pressures and change zones. In 2002, Shell started to develop the Smart Fields program, defining the technology and processes required in order to operate a Smart Field.
In 2003, the surface control was further developed so that remote operations from one of the Champion West jackets was possible from the main production platform and then from the Head Office. These successes led to the development of the Champion West Drilling Platform in 2005, a fully Smart, not normally manned platform. This platform incorporated almost all aspects of Smart technology that were available commercially at the time, with almost all aspects of the wells operated and monitored remotely. Surface flow control valves and sequencing valves to control surface rates and select wells for testing, a multiphase flow meter to accurately test wells, and the full suite of downhole tools that included inflow control valves to control flow, permanent downhole gauges for pressure data and fibre-optics to acquire distributed temperature surveys. Each well had up to five individual zones to maximize hydrocarbon recovery and value.
A study was carried out to establish the performance of a dragon well, or a well that dramatically changes inclination, in a thin oil rim reservoir. A well was simulated using commercial nodal analysis software by segmenting the inflow in multiple sections in order to incorporate the changes in trajectory and intersections with various reservoir layers. This simulation considered the pressure losses along the well bore for the varying trajectory, calculated for individual well sections, layers and combined commingle productivity and pressure profile; and was used to evaluate the well performance for a range of reservoir conditions (depletion, gas-oil ratio and water cut changes). This paper describes the approach used and key observations obtained from the results.
A "segmented?? inflow simulation approach can be used to model a dragon well. The method can be applied for modelling wells with sinusoidal trajectories in thin oil reservoirs. The results can be used to guide well and reservoir modellers in the concept assessment of this type of wells in field development studies. The model calculates the segment, layer, and total inflow and pressure profile in a complex trajectory. For the field and reservoir characteristics considered, the simulation indicated that the dragon well can produce through a wide range of conditions, including gas and water break-through. Good initial productivity can be expected from the well, but deteriorates fast with increasing GOR and water cut. As expected, the drawdown is not uniform along the trajectory; hence a drawdown stabilization strategy was addressed for the subject well through the use of a smart well completion. There is limited industry experience on sinusoidal or dragon wells modelling hence the results of the paper should be of interest to production technologists and reservoir engineers. This documented methodology can also be extended to simulation of complex horizontal or multilateral wells.