metaheuristic algorithms

With the booming exploration and development of unconventional hydrocarbon resources, the accurate estimation of source rock factors such as residual hydrocarbon potential (S2) from well logs has become increasingly important. Along with organic material properties, changes in lithology within an interval of possible source also induce well log responses. Artificial intelligence techniques may interpret these lithology-induced log responses as a signal for changes in the organic matter content and/or properties, resulting in decreasing their efficiency. In the present research, a new methodology called the litho-based method was proposed based on modeling the relationship between log data and S2 parameter for each type of lithology using Adaptive Neuro Fuzzy Inference System (ANFIS). The performance of the newly developed methodology was compared with those of the traditional ANFIS and hybrid methods for which the training process was carried out using a dataset including different lithologies. Results showed that the litho-based method successfully removed the adverse effects of lithological variations on the course of ANFIS training, resulting in estimating much more reliable S2 values. Among the traditional methods, utilizing particle swarm optimization (PSO) algorithm in conjunction with ANFIS showed higher performance. Nevertheless, the aforementioned hybrid approach is not as efficient as the litho-based method. The applicability of the proposed methodology was approved by applying it over Pabdeh source rocks for a well in SW Iran. Finally, it is recommended to use the litho-based method for estimating other geochemical factors as well as petrophysical parameters through log data.

Evaluation of Longshore Sediment Transport Rate in Imam Khomeini port using three examples of meta-heuristics algorithmImam Khomeini port is one of the ports of Khuzestan province in the Persian Gulf, which has 38 active wharves with a length of 7 km, is the largest active port in Iran and the annual dredging volume in this port is about 600 thousand cubic meters, of which about 400 thousand cubic meters is related to the Duragh canal and about 200 thousand are related to other ports of the port, since the sedimentation rate and dredging cost in this port is significant, so, in this study the rate and volume of sedimentation based on field information and modeling obtained from the Ports and Maritime Organization And comparing these values with the results obtained from meta-heuristic algorithms under MATLAB software in two-dimensional flow conditions and also the presence of all parameters affecting the transfer of sediment along the coast in Imam Khomeini port. After combining the three equations of CERK, Kamphuis and Bayram (CKB) and calculating the optimal coefficients and capabilities by Differential Evolution, Ant Colony Optimization and Genetic Algorithm, evaluation and validation with the values obtained from the available field data, coefficient error and computational capabilities Calculated using the root mean squares and bias method, therefore, it was observed that the coefficients and powers obtained from the genetic algorithm that were placed in the CKB equation have the most overlap with the rate longshore sediment transport from field data.