Mohammad Khanehbad

In this study, using a multi-layer perceptron neural network (MLPNN) model, total organic carbon (TOC) and hydrogen index (HI) values for Pabdeh and Gurpi Formations in the oil fields of Naft Sefid (NS-13), Kupal (KL-36, KL-38, and KL-48) and Palangan (PL-2) were calculated in the North Dezful Embayment located in the southwest of Iran. To build the MLPNN model, the geochemical data calculated by the Rock–Eval pyrolysis method (TOC and HI) and the conventional petrophysical well log data, including sonic transit time log (DT), formation density log (RHOB), total resistivity log (RT), spectral gamma-ray log, computed gamma-ray log and neutron porosity log from the NS-13 well were used. The log data were the input layer, and the geochemical data were the output layer of the model. Twenty-four datasets were used for MLPNN training, and seven datasets were used for MLPNN testing. Two hidden layers were considered in this technique. Each hidden layer has an activation function (tanh) and a solver parameter (lbfgs). The accuracy of measurement of TOC and HI indices of Pabdeh and Gurpi Formations in terms of R² was 0.93 and 0.90, respectively. This model has higher accuracy than the ΔlogR technique (R²: 0.28). Considering the relationships between the input data and other wireline logs is an advantage of this technique. These two formations have five source rock zones. Pabdeh Formation has three zones. The middle zone of the Pabdeh Formation (Pz. II) has the highest TOC (2.6 wt%) and source rock potential. Pabdeh Formation has kerogen type II. Gurpi Formation has a weaker source rock potential than Pabdeh Formation due to its low TOC content (< 1%). Both source rock zones of this formation have low TOC, but in some layers of the lower zone of the Gurpi Formation (Gz. II), high values for TOC were predicted. Gurpi Formation has Kerogen types II and III.

In the recent past, the applicability and uncertainty of pore-pressure prediction methods that work based on seismic signals have been serious challenges in carbonate reservoirs. In this context, enormous research has been conducted on the prediction of pore pressure in these rocks, some of which have ended with failure due to the complexity of carbonates. To address the main cause of such failures, we experimentally simulate the pore fluid in carbonates under reservoir conditions to interpret and classify the factors contributing to the pore pressure. The results are then built into a novel hybrid experimental model for evaluating the feasibility of pore pressure prediction — one that could bridge the gap between laboratory and empirical approaches. For this purpose, 34 core samples are collected from different geologic formations at different wells penetrating reservoir intervals. The mechanical properties of the samples are investigated by conducting experiments in a triaxial cell where pore pressure is applied under different regimes while simultaneously acquiring the ultrasonic response of the sample. Sensitivity analyses of pore pressure are carried out under drained and undrained conditions and the results are used to evaluate our methodology. Pore pressure coefficients of P- and S-wave velocities are introduced in two different conditions, namely constant differential stress and constant pore pressure, with the same dimension as compressibility. Results are interpreted statistically and effective parameters are identified from a pool of many variables; these include the static bulk modulus, Young’s modulus, Biot’s coefficients, porosity, permeability, and sedimentary textures (e.g., micritization, neomorphism, cementation, fracture, blocky calcite, and dissolution). A feasibility study template is further introduced for investigating pore pressure detectability based on commonly available data in carbonate reservoirs by incorporating the main controlling factors. Our template and findings of this research are important for pore pressure prediction before performing further research and implementing operation programs. They also can decrease the risk of underestimating the pore pressure, thereby improving the well planning performance and mitigating the drilling hazards.

Qadir Member of Nayband Formation, located in East of Central Iran, has developed to a great extent. Investigation of the lithofacies and sedimentary environment, resulted in identification of the deltaic and marine deposits. Based on field evidence and facies features, Qadir Member consists of two lithofacies, including carbonate and siliciclastic facies. The siliciclastic facies were identified as having four sandstone facies including Sr, Sh, Sp, St, three fine-grained lithofacies, including FI, Fm, Fl (Sr) / Sr (FI) and one coal facies. Also, regarding the field, laboratory studies, and identification of lithofacies, the coastal plain, deltaic (including deltaic plain, proximal delta front, distal delta front, and prodelta) and open marine environments were identified for Qadir Member which is is under the impact of tidal currents. The chemical weathering index (71%) indicated semi-arid to semi-humid conditions and plotting the geochemical data showed the provenance of re-cycling and active continental margin and because of Chemical Index of Alteration, the weathering rate was found to be rather medium to high. The geochemical diagrams also showed a probable source of the intermediate igneous and sedimentary rocks. The active continental margin conditions for this deposit could suggest the Neotethys subduction under Iran’s plate and volcanic activity at the end of Triassic, which coincided with the early Cimmerian orogeny in Alborz and Central East Iranian Microcontinent.