In thin bedded reservoirs high resolution borehole images are generally used to determine distribution of high quality, productive sands. Deep water Tertiary reservoirs of Krishna Godavari basin are generally very complex and heterogeneous, ranging from massive thick sands to highly laminated very thin sand/shale sequences with average bed thicknesses less than the vertical resolution of microresistivity-imaging tool. The image tool responds to the beds and features smaller than the vertical resolution but can not accurately determine laminar bed thickness which can be used as an integral part of lowresolution technique. In the absence of a well defined set of thin bed boundaries from image tools, an integrated of approach of forward modeling and inversion is also impractical for the accurate thin bed evaluation.A new approach of laminated shaly sand analysis is developed where laminar sand/shale estimation is based on generation of binary lithology on shale volume curve derived from micro-electrical resistivity. Formation beds as thin as the twice of the sampling interval can be generated through this technique in such a way that cumulative net sand fraction is restricted with integrated sand volume. Laminar shale volume from image tool calibrated to the LamCount core data has been considered to be the ground truth in the analysis. The volume of dispersed shale and the total and effective porosities of the laminar sand fraction are determined using a Thomas-Stieber volumetric approach. Water saturation is estimated from laminar sand-fraction resistivity derived from electrical anisotropy.Application of the methodology leads to an improved accuracy of reserve estimates and productivity predictions. Entire procedure is illustrated with an example of completely cored reservoir section. Results indicate that low resistive laminated reservoirs with very high shale fractions can be highly productive with reservoir quality equivalent or some times even better than massive thick beds.

The objective of this paper is to present a unique Petrophysical Grouping (PG) approach in a carbonate reservoir located in transition zone. It is very challenging, especially in Carbonate reservoirs, exist in transition zone, to establish PG definitions due to the complexities result from reservoir heterogeneities and diagenesis. Consequently establishing a suitable Saturation Height Function to match the Log derived Saturation is another challenge. In addition, the limited coverage of Mercury Injection based Capillary Pressure data (MICP) as compared to Routine Core analysis (RCA) data provides difficulties in establishing appropriate PG definition. In first step the MICP data was used together with porosity/permeability to define distinctive groups. The PGs were further up-scaled using deterministic and Neural Network (NN) approaches. The best method was chosen by performing a test that compares the Washburn Pore Throat Radius (PTR) with the predicted PTR. To estimate a most representative log based permeability model, independent of water saturation a NN and Self-Organization Map methodologies were adapted. The limitations of MICP samples were handled by using an analog of a larger field with 100s of MICP samples. This was used to propagate the PGs to log domain by utilizing the permeability model. Five PGs were defined using deterministic approach in which the best one is characterized having low displacement pressure, low irreducible water saturation, high pore throat radius and high porosity and permeability responses. Winland was shortlisted after testing other methods as the most applicable PG method in the reservoir as it provides the best correlation with lab PTR (94%) and the shape of WR35, consequently provides good match with computed Sw log and the shape of the PRT curve (Gunter, 2017). Regardless of the good response of NN approach the method was not chosen due to limitation of MICP data. A good relationship of Winland based PGs were found with geology and associated facies indicate strong affinity with the depositional environment and diagenetic overprints on each existing facie associations, hence a permeability model is depicted with confidence. The permeability model was executed for two geographic sectors using density, neutron porosity and GR as main inputs. SOM and NN Permeability were blind tested which resulted in more than 80% match. The predicted Sw matches with log based Sw over the entire field thus the PGs definition and propagation to log domain are considered valid.

This is a part of the project which developed an Information & Communication Technology based software application tool focused and addressing the issues related with the preparation of Environmental Monitoring Report and Environmental Management Plan in NorthEastern Coalfields under Coal India Limited. It addressed mine water treatment with cost analysis, Overburden Dump Management along with the parameters of Air, Water, Effluent Water, and Soil. This user friendly software tool enabled with GIS or Geographical Information System. The present paper dealt with the air pollution and its variations in a spatio-temporal scenario. In a highly vegetation domain which covered not only by forests but also wild life and tea gardens, air pollution played a pivotal role in maintaining the ecological balance, specially for micro organisms. Therefore the work opened a broad scope of research from air pollution to micro organism balance in an eco sensitive region.

Because of advance computing technologies, machine learning today is widely used for many types of prediction. The performance of existing prediction techniques has been quite acceptable but still it needs improvement. In oil industry, this type of machine learning is being used for Permeability prediction. In case of homogeneous reservoir, conventional permeability prediction techniques are very well documented in the available published literature and are easy to be implemented however, it is a real challenge in case of complex carbonate reservoir which are highly affected by diagenetic overprint. The machine learning algorithms exactly does according to what user had trained for it. It get confused either if it receives a new unseen input for which it was not trained or it has multiple answer to the same input. This study shows that by using selective intelligent inputs (quantitative factors influencing permeability) and with proper segregated and intuitive training to machine it is possible to gain better correlation between different inputs and a reliable permeability prediction can be achieved. The studied Early Cretaceous reservoir has a large areal extent with 20% average porosity and large range of permeability, from 0.1 to 1000 md due to preferential diagenetic processes over large variety of lithofacies. Capturing high permeability streaks/zone simultaneously with low K intervals was quiet important to understand the fluid flow dynamics and hence was a real challenge in this project. This paper describe the workflow developed to analyze data in different dimensions away from the conventional ways, detailed QC steps and preparing the intelligent input data sets (Normalized depths, Rock types, zonal and sectorial flags, synthetic permeability guide curve, etc.) which facilitate the machine to make a best decision and output optimum results. This paper shows two- point normalization method to bring reservoir depth interval between 0 to100 and pick up consistent permeability profile in each sector which correlates better to stratigraphic framework. In this study, the models were built with 3081 CCA data from 30 spatially distributed cored wells and blind tested on 10 other cored wells; later these models were used to predict permeability in 210 uncored wells. Comparison of the predicted permeability with the well test permeability shows Ktest/Klog in the range of 1.05 to1.6 suggesting fair to very good reconciliation. The normalized depth vs. predicted permeability and an independent cumulative permeability QC plots are introduced to qualify the prediction model. The workflow shown in this paper is logically built and gives the interpreter flexibility to choose inputs based on the observed permeability variance. The QC flags and the predictive models are easy to implement and update permeability model as the new data comes.

Conventional geo-steering approach use raw logging measurements to define wellbore positioning within the reservoir while drilling. The geo-steering specialist usually compares real-time logs to modelled logs (GR/Density/Neutron/Resistivity) and the geological model is then adjusted to make real-time decisions to deliver the well objectives. This conventional method is applicable to most reservoir conditions. However, it may be insufficient or inappropriate in heterogeneous reservoirs or wells with complex geological settings, potentially resulting in wells being sub-optimally placed and reducing the value of reservoir sections in terms of productivity. This paper aims to showcase a Petrophysics-based Geo-steering approach to maximize the value of reservoir sections. Geo-steering aims to place the well trajectory in the lithology with optimum storage capacity, flow capacity and hydrocarbon saturation. The method of log-to-log comparison is popular for its simplicity and speed of use in real-time but is not enough for certain scenarios. For example, the real-time log response can be very different from modelled log response in the presence of gas or very light oil, irrespective of petrophysical properties (porosity/permeability) being similar. Moreover, real-time Sw estimation would be required in addition to porosity to minimize the risk of drilling a producer into water bearing intervals. In fact, the comparison between petrophysical parameters is more appropriate to heterogeneous reservoirs or wells with complicated geology. This approach requires good co-ordination between geologist, petrophysicist and geo-steering specialist. Prior to drilling, the petrophysical model from offset wells should be defined and used to derive porosity, permeability and saturation. While drilling, the petrophysical properties are then interpreted in real-time and based on the comparison between modelled and real-time petrophysical properties, decisions are to be made with respect to the well objectives. An example with strong gas effect in a carbonate reservoir from Abu Dhabi is presented to demonstrate this novel approach. Real-time density/neutron does not have good correlation with modelled density /neutron due to gas effect. Such poor correlation can be attributed to proximity to a Gas Oil Contact (GOC) and dynamic invasion, complicating the real-time geo-steering. However, real-time total porosity from log analysis correlates very well with modelled total porosity, providing confidence in wellbore positioning and allowing the geologist and the geo-steering specialist to make the correct real-time decision to place the well in the optimum stratigraphic position in order to meet the well objectives. Only conventional logs are utilized in this case, but if real-time NMR and resistivity image interpretation are available, it will provide additional information in term of permeability, secondary porosity and irreducible water saturation to aid efficient geo-steering.