Abstract
AbstractA mechanical earth models (MEM) is a crucial component of any well design process. Because it describes the subterranean formations in-situ stresses and rock mechanical properties, it is the main input into any geomechanics model. This means high impact issues like wellbore instability, drilling induced fractures, and hydraulic fracturing success probability are all directly dependent on the reliability of the provided MEM. Considering that MEM have been historically developed based on empirical correlations and an expert manual interpretations of logging data, intuitively, their accuracy has a large margin for improvement. Specifically, because the determination of the in-situ maximum horizontal stress is based on manual interpretations of caliper or image logs, the result of this interpretation can vary greatly from one user to the other. Therefore, this work aims to improve this process by presenting an automated algorithm that interprets image and caliper logs data and produce a consistent determination of different MEM parameters.The proposed algorithms utilize both resistivity and ultra-sonic based image logs along with wireline caliper measurements to first quantify areas of enlargements or wash-outs within a wellbore section. The algorithms perform this quantification by reading the Red-Green-Blue (RGB) values of each pixel in the image and then assign a radial measurement to that reading. The generated radial measurements are calibrated using the caliper log data. The end result of this process is radial measurements that describe the wellbore dimensions across the 360° range. These radial measurements are then filtered for enlargements by comparing them to the size of the drill bit that was used to drill the hole section being analyzed. Finally, the output from these algorithms is utilized within a stress polygon to determine the in-situ maximum horizontal stress.To showcase the value of consistent estimations, MEM produced through the proposed algorithms and an expert manual interpretation are incorporated into geomechanics models. These geomechanics models are intended to issue recommendations regarding wellbore instability while drilling or hydraulic fracturing job design. The comparison between the results of each model are then examined against the outcome of each job in the field. The MEM produced from the algorithms shows higher accuracy when predicting wellbore instabilities and breakdown in hydraulic fracturing job.In addition to improving the accuracy of different geomechanics models, the novel method presented in this work also creates a pathway to incorporating information from image logs into sophisticated numerical modeling solutions. It is shown that three-dimensional (3D) geometries interpreted these logs can be incorporated into 3D meshes for the purpose numerical modeling of wellbores through methods like the finite element model.