The present paper deals with dynamically balanced ascending and descending gait generations of a 7 DOF biped robot negotiating a staircase. During navigation, the foot of the swing leg is assumed to follow a trajectory, after ensuring its kinematic constraints. Dynamic balance margin of the gaits are calculated by using the concept of zero-moment point (ZMP). In the present work, an approach different from the well-known semi-inverse method has been developed for trunk motion generation, in which it is initially generated based on static balance and then checked for its dynamic balance. The joint torques are determined utilizing the Lagrange-Euler formulation, and the average power consumption at each joint is calculated. Moreover, variations of the dynamic balance margin are studied for both the ascending as well as descending gaits of the biped robot. Average dynamic balance margin and average power consumption in the ascending gait are found to be more than that of the descending gait. The effect of trunk mass on the dynamic balance margin and average power consumption for both the ascending and descending gaits are studied. The dynamic balance margin and average power consumption are found to decrease and increase, respectively with the increase in the trunk mass.

In the present paper, two algorithms based on soft computing have been developed for dynamically balanced gait generations of a biped robot ascending and descending a staircase. The utility of the soft computing tools is best justified, when the data available for the problem to be solved are imprecise in nature, difficult to model and exhibit large-scale solution spaces. The problem of online gait generation of a biped robot exhibits such a complex phenomenon, and ultimately soft computing has become a natural choice for solving it. The gait generation problems of a biped robot have been solved using two different approaches, namely genetic-neural (GA-NN) and genetic-fuzzy (GA-FLC) systems. In GA-NN, the gait generation problem of a two-legged robot has been modeled using two modules of Neural Network (NN), whose weights are optimized offline using a Genetic Algorithm (GA), whereas in GA-FLC, the above problem is modeled utilizing two modules of Fuzzy Logic Controller (FLC) and their rule bases are optimized offline using a GA. Once optimized, the GA-NN and GA-FLC systems will be able to generate dynamically balanced gaits of the biped robot online. The performances of the two approaches are compared with respect to the Dynamic Balance Margin (DBM).

Brunei Shell Petroleum (BSP) encompasses multiple offshore and onshore oil and gas field. The study asset produces more than 50% of company's hydrocarbon production. Currently, gas production from the asset is at maximum system limit governed by downstream gas demand and surface-facility constraints. Producing the wells at right configuration can help in maximising condensate production while honouring gas demand thereby maximising revenue for the asset. The analysis of data from past few months indicated that actual condensate production was quite lower than asset-wide Integrated Production System Model (IPSM) estimates. Frequently, wells were operated in wrong configuration which had resulted in system/wells operating outside Operating Envelopes (OE's), excessing choke wears, system trips and higher production deferment. A production optimisation study using LEAN approach was initiated to maximise condensate production and to increase process safety compliance. Multiple brainstorming sessions were organized to map data flow from well-testing to production optimization to offshore operations. The process map highlighted that scheduled activities, operational requirement and communication issues often resulted in well configuration different than IPSM suggested configuration. The LEAN approach helped in identifying system wastes, viz. absence of real-time feedback to operators, different tools/software used by different team, reaction time to new testing data, misunderstanding of well OEs etc. which were resulting in lower condensate production. A fit-for-purpose Real Time Optimization (RTO) tool was developed which used pressure and flow data from wellhead sensors and from real-time production estimation tool (viz. Production Universe) to generate best configuration of wells for maximising revenue. The optimum configuration accounted for well and facilities OE ensuring better process safety compliance. The tool output could be compared against real-time measures (e.g. Tubing head pressures) and providing continuous feedback to operators. The RTO tool was implemented in Q2 2016 with full support of asset leadership. Multiple offshore trips were made to explain the tool to frontline operation staff, operation supervisors and production leads. The project helped in increasing condensate production by over 20% and created a reliable and sustainable process for continuous optimization directly by operations staff. It also ensured that wells always produced within OEs thereby increasing process safety compliance. Operating the wells and chokes optimally also led to cost savings from reduced choke replacements and lower production deferment. The tool served as a common reference for all teams thus eliminating communication waste. The use of real-time sensors and Production Universe and RTO can help in reducing reaction time to system changes and help in maximising revenue. A sustained and integrated effort is needed to drive the process in the organisation and achieve production gain. The LEAN process can be useful to in root cause identification of problems and for drafting mitigation measures.

On a daily basis, the Well, Reservoir and Facility Management (WRFM) team screens all producer and injector wells to identify those wells that are under-performing or outside safe operating limits. A 'Well Operating Envelope' tool has been developed, focused on establishing the boundary conditions of various safety-critical parameters and optimized well performance. Current well operating conditions, derived from real-time gauges and flow estimates from Production Universe (PU), are monitored vis-à-vis these parameters via this tool to identify the "problematic" wells. Various diagnostic tools (i.e. Surface P-Q curves, VLP-IPR Nodal Analysis, Tulsa Flowline erosion model, Gas-Lift response curve, Inflow performance, SC-SSSV pressure plot and Hall plot derivatives) have been used and visualized for better understanding of the well flowing conditions for both production and injection systems. This tool fetches data from multiple databases like Data Historian, Energy Components and Production Universe (PU); it is also capable of interfacing with Prosper and WinGlue to fetch well performance data automatically. Following improvements have been observed with implementation of this tool:Traffic light and graphical representation enables quick well surveillance in identifying problematic wells and taking corrective measures.Monitoring of the daily PU estimated production vis-à-vis well tests, allocated production volumes and the physical well models will increase hydrocarbon accounting efficiency.Monitoring real-time parameters like FTHP, FBHP, and flowrates allows the Petroleum Engineers and Operations to operate safely within defined boundaries.Monitoring of parameters like GOR, drawdown will enable adherence to reservoir management guidelines and reduce potential reserve losses. Visualization of information from multiple data sources into this real-time tool provides an effective and efficient monitoring to ensure optimal well production within safe limits on a day-to-day basis for well, reservoir and facility management.

Brunei Shell Petroleum (BSP) has a complex production gathering network that comprises of multiple onshore and offshore Assets. Gas and oil is produced under different pressure regimes, and with varying compositions. The production from each asset can be routed in multiple ways to different customers, each with their own compositional requirements. Crude oil and gas is routed to Brunei Liquified Natural Gas (BLNG) and Seria Crude Oil terminal (SCOT) for further processing before export to external customers. Apart from export, other customers include BMC (Brunei Methanol Company), Brunei Shell Refinery (BSR) and Department of Electrical Services (DES), fueling Brunei's vehicles, homes and local industries. Currently this complex production network is managed by a dedicated Operations Control Centre (OCC). The central OCC team coordinates, monitors and controls the hydrocarbon activities to meet customers' quality and quantities demand by engaging with the 4 individual Asset Operation teams, as well as customers, to optimize production at the Pan-BSP system level. With the introduction of the Integrated Production Monitoring and Optimisation System (IPMOS) tool, the Pan-BSP production optimisation can be automated, in real-time. The IPMOS tool performs a high-level production optimisation built on top of existing real-time well production surveillance models, and it dynamically adjusts the system capacity in the event of upsets. In this way, the tool provides day to day advisory of production optimisation during both normal operations as well as during different production upset scenarios, and it also eliminates the need for OCC team to maintain multiple MS Excel sheets/macros to cater for those scenarios. This is a step forward in production system optimisation through digitalisation.