Annulus Cementing with Coil Tubing Packer Methods with Case Study on Sisi Nubi Field

Author(s):  
R Rachman

PHM fields are located in The Mahakam Delta and Offshore area in East Kalimantan, Indonesia. Most of the wells are multizone gas producers completed with cemented tubing which cover only the main zone, but for shallower reservoirs, these have not been targeted until recent years. To unlock a shallow reservoir target, annulus cementing is required as a well barrier prior to perforation. There are several conventional methods of annulus cementing which have been implemented in Mahakam for well intervention activities, such as: a. Annulus cementing with balance meod; b. Annulus cementing with cement retainer. The Sisi Nubi well is one case study of annulus cementing with conventional method using the balance method that experienced failure during the cementing job and created the problem of cement flow back to the tubing. Consequently, this required additional intervention to clear access by milling and underreaming operations that build high cost. The Well Intervention division is continuously looking for new methods to reduce operating time and cost efficiency. Annulus Cementing with Coil Tubing (CT) packer is a new method to perform annulus cementing resulting in clean wellbore and high quality cement in the annulus. This method has been successfully implemented in Sisi Nubi wells with satisfactory results.The principle of annulus cementing with CT packer is temporary set on the well bore to prevent upward and downward movement of fluid or pressure during and after cementing operation, and also to avoid cement contamination with wellbore fluid. Following the success in offshore field application, the division then extended the application in the fields at the Delta area.

2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>


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