We introduce an innovative OpenMP design and implementation of a search-growing algorithm for fine-grained lock-based parallelization, which enhances automatic volume extraction in seismic data represented by 3D voxel pictures to detect salt bodies and assess the voxel texture. The first algorithm was developed in MATLAB, subsequently translated to Fortran, and ultimately enhanced with OpenMP pragmas to facilitate the parallel version. In the parallel version, synchronization techniques are essential to enforce limits on the sequence of voxel analysis conducted by the threads. To do this, we employed OpenMP locks to ensure that only a single thread can process one voxel, hence maintaining the coherence of the data. Minimizing crucial regions in parallel programming is advisable as they might hinder scalability and performance. In this application, locks do not induce latency since not all threads attempt to access the same voxel, indicating that not all threads traverse the same critical region. Our design involves securing each voxel that may adapt to the 3D image, enhancing performance and markedly decreasing execution time. Performance trials were conducted on a dual Xeon system featuring twenty physical cores, with Hyperthreading (HT) both disabled and enabled. In this investigation, HT improved slightly the performance.