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Different types of Demand Response Programmes (DRPs) exist and can be simultaneously offered by the electrical utilities through established contracts with customers. Operating simultaneously multiple types of DRPs might lead to undesired results. DRPs might have different responses to objectives, time-based ones tend to maximise consumption during lowest tariffs periods while incentive-based ones tend to reduce the usage based on peak events, accordingly contradiction might occur. Thus, synchronising these DRPs and their parameters through an optimised process including customer selection for the appropriate one is a mandatory step. A fair allocation of the various types of DRPs including their execution's priority at a specific time is the main objective of this study. An original approach based on clustering technique for predicting customers' behaviour coupled with a particle swarm optimisation (PSO) to reach an optimal solution for relocation is presented. In this study, an optimal solution is developed; it provides the various DRPs with the most convenient parameters for the best demand/ generation balance, utility profit maximisation and operational cost minimisation. The method is validated through a simulation applying a time-based with two incentive-based DRPs in the presence of conventional and renewable generation while using Kmeans clustering and PSO on Matlab.

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Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction. INDEX TERMS Multiple demand response programs, MOPSO, group method of data handling (GMDH), Kmeans, clustering, smart grid, industrial customers.

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The Adanga Field was discovered in 1974 and is located offshore Calabar in the central part of OML-123 in Nigeria territorial waters. Addax Petroleum took over operatorship of the fields in 1998, when the production from Adanga was just 2,000 BOE per day. Through introduction of new technology, field production increased to over 20,000 BOE per day by 2009. Poor productivity and increasing water cut is an issue in Adanga. Re-interpretation of 3-D seismic data and incorporating additional information from new wells 3-D reservoir modeling confirmed an economic hydrocarbon pool to the west of Adanga (ADW). Additional reservoir targets were identified in the Adanga South field. The plan was to develop these reservoirs from existing infrastructure. Unfortunately, the existing Adanga South platform had no remaining slot capacity to drill new wells without additional infrastructure investment. Upgrading the platform with additional drilling slots was deemed uneconomical, unsafe and will increase collision risk. The platform on adjacent field, Adanga South West, did have drilling slots available, so it was decided to investigate the feasibility of drilling extended reach wells from the Adanga South West platform to access these otherwise stranded reserves in the Adanga South field Challenges in drilling these wells include the weak nature of the formation which can pose difficulties to conventional directional drilling techniques, wellbore quality and ECD management. This paper describes the planning, engineering and execution of constructing two; complex, 3D, horizontal, extended reach wells drilled on the Adanga Field in 2010 to access locked-out reservoir in adjacent field. Using the experience from these two wells, the challenges of drilling complex, high angle wells in the weak sediments of the Niger Delta will be discussed along with how these wells were ultimately drilled and geologically positioned for optimum recovery.

Electric line remedial work such as through tubing perforation has been successfully carried out in most vertical/deviated wells. However, in high angle/horizontal wells it has become a major undertaking due to inability of the gravity-assisted, electric line to convey perforating guns to angles greater than 65°. With this electric line limitation, the options available for deploying the guns are limited to wireline tractor and e-coiled tubing since most through tubing perforation are done in real time. Apart from space constraint at the wellsite and cumbersome logistics, the main set back with the e-coil is its unavailability, while the tractor has high operational cost. This paper outlines the successful perforation of horizontal wells in the Niger Delta while addressing the operational issues encountered. The first case history is Addax ORW-11H, a horizontal well planned to have the lateral section slimmed down to 6 in. hole. After successfully drilling the hole to target depth (TD), a 6-in. hole-opener was deployed on 3½ in. drill pipe to condition the well before running the 4½ in. liner. In an attempt to re-run the hole-opener, the bottomhole assembly (BHA) got stuck 20 ft off target depth. After several unsuccessful attempts to recover the BHA, it was decided to perforate the 3½ in. drill pipe to provide a conduit for production. The challenge was deploying the gun at 90°deviation, correlating and perforating on depth without e-coil. This was overcome by using an intelligent memory correlating and perforating tool to perforate the drill pipe and communicate with the reservoir. On completion, the well delivered 1,400 bopd with 0% water cut. The second case history is Chevron Okan Well Y, which was drilled and completed as a horizontal gravity waterflood injection well. The initial 20,000-bwpd water injection began to drop and later quit due to sand accumulation and plugging. After an unsuccessful sand cleanout, the proposed remedial action was to add 40 ft of additional perforations shallower in the target reservoir to provide access for the desired injection rates for the well and help increase recovery. Initial attempt to run electric line to TD failed due to inclination of over 77°, but the well was later perforated successfully using the same novel technology with significant cost reduction.