Inversion-based Interpretation of Borehole Spontaneous Potential Measurements for Accurate Estimation of Water Resistivity in the Presence of Shoulder-Bed and Mud-Filtrate Invasion Effects

Electrical resistivity of formation water is a fundamental property used to quantify in situ water quality for human consumption or for assessment of hydrocarbon pore volume. Resistivity interpretation methods commonly used to quantify the electrical resistivity of formation water invoke rock porosity and fitting parameters that require additional and independent core measurements. Alternatively, the spontaneous potential (SP) log can be used to calculate water resistivity without knowledge of rock porosity in wells drilled with water-based mud. In combination with resistivity and gamma-ray logs, SP logs can be used to estimate water quality, apparent volumetric concentration of shale, and for qualitative assessments of permeability. However, SP logs often exhibit both shoulder-bed and mud-filtration effects; these effects need to be mitigated before using SP logs for calculation of water resistivity. We develop a new inversion-based method to simultaneously mitigate shoulder-bed and mud-filtrate invasion effects present in SP logs via fast numerical simulations based on Green functions. The interpretation method is implemented on SP logs acquired across aquifers with various degrees of complexity using noisy synthetic and field measurements to estimate equivalent NaCl concentration, radius of mud-filtrate invasion, and sodium macroscopic transport number. Interpretation results compare well to those obtained from resistivity and nuclear logs, provide estimates of uncertainty, and can incorporate a priori knowledge of aquifer petrophysical properties in the estimation.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Intelligent Prediction for Rock Porosity While Drilling Complex Lithology in Real Time

Rock porosity is an important parameter for the formation evaluation, reservoir modeling, and petroleum reserve estimation. The conventional methods for determining the rock porosity are considered costly and time-consuming operations during the well drilling. This paper aims to predict the rock porosity in real time while drilling complex lithology using machine learning. In this paper, two intelligent models were developed utilizing the random forest (RF) and decision tree (DT) techniques. The drilling parameters include weight on bit, torque, standpipe pressure, drill string rotation speed, rate of penetration, and pump rate. Two datasets were employed for building the models (3767 data points) and for validating the developed models (1676 data points). Both collected datasets have complex lithology of carbonate, sandstone, and shale. Sensitivity and optimization on different parameters for each technique were conducted to ensure optimum prediction. The models’ performance was checked by four performance indices which are coefficient of determination (R2), average absolute percentage error (AAPE), variance account for (VAF), and a20 index. The results indicated the strong porosity prediction capability for the two models. DT model showed R2 of 0.94 and 0.87 between the predicted and actual porosity values with AAPE of 6.07 and 9% for training and testing, respectively. Generally, RF provided a higher level of strong prediction than DT as RF achieved R2 of 0.99 and 0.90 with AAPE of 1.5 and 7% for training and testing, respectively. The models’ validation proved a high prediction performance as DT achieved R2 of 0.88 and AAPE of 8.58%, while RF has R2 of 0.92 and AAPE of 6.5%.

Effect of Porosity on Uniaxial Compressive Strength in Sedimentary Rocks

The rock strength parameter is an important factor used in determining the geotechnical design in determining the stability of the underground slope or mine. One of the rock strength tests in the laboratory (intact rock) is the uniaxial compressive strength test. One of the factors that influence rock strength is the porosity of the rock itself, especially in sedimentary rocks. The rock porosity test is carried out by testing the physical properties of the rock, then a regression analysis is carried out to obtain the correlation of the effect of porosity on rock strength and the correlation between porosity and absorption considered in the analysis. The linear regression results obtained between porosity and saturated water content of rocks showed a positive correlation where the increase in porosity, the saturated water content also increased. The correlation between porosity and uniaxial compressive strength obtained a strong correlation with the power regression model as the best model compared to other regression models because it has the lowest error based on the Root Mean Square Error (RMSE). The final result is obtained by comparing the effect of porosity on rock strength, that is the higher the porosity value have the smaller porosity, so that an increase in rock porosity will reduce the strength of the rock.

STUDY ON HYDROGEOLOGICAL ASPECT OF GALANG SUB-DISTRICT, BATAM CITY FOR ACCEPTANCE OF PROSPECTIVE NPP SITES

This is a preliminary study in the selection of Nuclear Power Plant (NPP) site in Batam's Barelang area to support industrial growth in the area in the future. The initial site selection was conducted in 2015 and 2017 in the pre-survey phase, considering hydrogeological aspect. The results of previous research showed four potential areas, namely Pasir Panjang Beach, Tanjung Batu, Dapur 3 and Tanjung Rame. The hydrogeological aspect plays an important role in the consideration of site acceptance. This is related to the consideration of potential flow of radioactively contaminated groundwater seepage in the site area, in the event of a potential release. The acceptance of the NPP site from the hydrogeological aspect is based on the site’s permeable geological formation and porosity condition, where groundwater can be stored. The purpose of the study was to assess the potential site in Galang Sub-district, Batam City based on hydrogeological aspects which include surface geology, groundwater productivity and rock porosity. Research methods include primary and secondary data collection, literature review and ranking analysis. The results showed that Tanjung Batu, Dapur 3 and Tanjung Rame could be chosen as the potential sites for NPP based on the hydrogeological aspect. The three regions have surface geology in the form of sandstone, clay and claystone rock with medium rock porosity level and medium groundwater productivity. Pasir Panjang Beach is less preferable because it has a high porosity of rocks with the productivity of aquifers is being spread widely. Key Words: Hydrogeology; Site selection; Acceptance of NPP site

Seismic AVO statistical inversion incorporating poroelasticity

Seismic amplitude variation with offset (AVO) inversion is an important approach for quantitative prediction of rock elasticity, lithology and fluid properties. With Biot–Gassmann’s poroelasticity, an improved statistical AVO inversion approach is proposed. To distinguish the influence of rock porosity and pore fluid modulus on AVO reflection coefficients, the AVO equation of reflection coefficients parameterized by porosity, rock-matrix moduli, density and fluid modulus is initially derived from Gassmann equation and critical porosity model. From the analysis of the influences of model parameters on the proposed AVO equation, rock porosity has the greatest influences, followed by rock-matrix moduli and density, and fluid modulus has the least influences among these model parameters. Furthermore, a statistical AVO stepwise inversion method is implemented to the simultaneous estimation of rock porosity, rock-matrix modulus, density and fluid modulus. Besides, the Laplace probability model and differential evolution, Markov chain Monte Carlo algorithm is utilized for the stochastic simulation within Bayesian framework. Models and field data examples demonstrate that the simultaneous optimizations of multiple Markov chains can achieve the efficient simulation of the posterior probability density distribution of model parameters, which is helpful for the uncertainty analysis of the inversion and sets a theoretical fundament for reservoir characterization and fluid discrimination.