Driving Deep Transient Testing with a Complete Digital Workflow – A Sustainable Exploration in Green Fields
Authors: Ling D. , Daungkaew S. , Kristensen M. , Lefranc M. , Sherik A. , Cavalleri C. , Hademi N. , Gisolf A. , Etchebes M. , Dubost F. , Nordin T. , Talib M. A. Abu , Ong L. W. , Motaei E. , Rajan S. K. T. 
Published: February 2024

Reservoir characterization is a main objective in oil & gas exploration where permeability-thickness, skin factor, reservoir boundaries, production potential and reservoir fluids are the key values. However, sustainability currently plays an important role which often translates to zero flaring compliance. Deep Transient Testing (DTT) is a new generation wireline formation testing technique investigating higher permeability and thicker reservoir packages with increased radius of investigation (ROI) compared to traditional Interval Pressure Transient Testing (IPTT) by flowing faster and longer and by using higher resolution pressure gauges. DTT is not a replacement for Drill Stem Testing (DST), but rather an approach to bridge the information gap between IPTT and DST, mainly used in green fields, especially in multi reservoirs where it is costly to test all potential zones.

Offset well data are used to build a preliminary numerical model for feasibility study in the target well. As soon as recorded, relevant logs such as Nuclear Magnetic Resonance (NMR) and image logs are used to select pressure, sampling, and DTT points and to derive reservoir properties and facies description to update the numerical model. Innovative workflows, from thin beds sand count and resistivity to permeability anisotropy calculation, are employed. During the DTT acquisition, real time monitoring and constant communication between parties is critical for an effective operation. In-situ reservoir fluid data and pressure transient data are constantly analyzed and integrated into 2D and 3D space in an open and collaborative digital cloud environment. Additionally, a 3D geo-context is created using geological and petrophysical data, optionally combined to seismic information. The last step of the workflow consists of dynamically calibrating this static model to obtain insights such as well deliverability, reservoir connectivity or minimum hydrocarbons in place.

Two case studies are presented to demonstrate the value of the workflow which is applied to a complex reservoir consisting of multi layers with cross flow production potential. The DTT objectives include determining the reservoir flow potential, de-risking the presence of baffles, and to estimate the volume of influence of the test. DTT analysis and history matching is used to address reservoir uncertainties for field development planning. In addition, the total DTT produced CO2 emission is compared to traditional testing methods and shows a 70-80% reduction in CO2e (CO2 equivalent) quantities. For Malaysia alone, a total of 2,130 metric tons of CO2 emissions were reduced, equivalent to 481 cars off the road annually with 3 DTT stations.

This paper presents a complete digital workflow applied to several green fields in Asia-Pacific region that leads to successful DTT operations initiated from intelligent planning which positively impacted the field development decisions.