Abu Dhabi Company for Onshore Oil Operations

About: Abu Dhabi Company for Onshore Oil Operations is a based out in . It is known for research contribution in the topics: Carbonate & Carbonate rock. The organization has 184 authors who have published 111 publications receiving 1562 citations. The organization is also known as: ADCO.

Wireline Straddle Packer Microfrac Testing Enables Tectonic Lateral Strain Calibration in Carbonate Reservoirs

Abstract: Wireline straddle packer microfrac tests have become an important technology in creating microfractures to measure in-situ formation breakdown pressure, fracture re-opening pressure, fracture closure pressure and stress contrast between reservoir and non-reservoir intervals. The formation tensile strength can also be estimated from the difference between breakdown and re-opening downhole pressures. This case study describes the use of microfrac tests measurements to validate and calibrate the horizontal stress profile in various intervals of the carbonate reservoir. Well-injection plans, cap-rock integrity assessment, shale reservoir fracture containment, stress contrast and minimum and maximum horizontal stress estimations can all be quantified from microfrac test measurement. Six straddle packer stations were tested for microfracturing in this study well. One microfrac test was repeated in one formation due to observed poroelastic effects in the fracture re-opening pressure responses. Poroelastic effects around the borehole occur when the pore pressure near the borehole increases with the injection cycles, thereby making it more difficult to effectively re-open the pre-existing induced fracture. When poroelastic effects are evident, it is important and recommended to record the first pressure fall-off cycle after the formation breakdown for fracture-closure identification. Subsequent cycles will indicate higher fracture closure pressures and therefore overestimate the minimum horizontal stress in the interval. This paper describes the pre-job modeling, real-time monitoring and post-job interpretation of straddle packer microfrac testing for recalibration of the geomechanical model to provide continuous logs of in-situ horizontal stress profiles over the entire interval.

A decision-making approach using point-cloud-based granular information

Abstract: Real-life decision problems are often solved by using a very limited set of data points. The computational complexity of such decision problems can be handled easily by using a mathematical entity called stochastic point cloud (SPC). In general, SPC is a form of granular information and is simulated by a data-driven stochastic process. In this study, particular interests are given on normal-distribution-driven SPCs. When the parameters of a normally distributed variable is not clearly known due to the lack of information or due to the multiplicity of regression analysis, it forms a set of SPCs. Three types of such SPCs are described in detail in this study. The effectiveness of such SPCs in solving a real-life decision problem (i.e., how to minimize vehicle emissions in Abu Dhabi Emirate of United Arab Emirates) is also shown. To develop more realistic and man-machine-friendly decision support systems one can use SPCs.