Vertical Interference tests (VIT) are used to determine the hydraulic connectivity between the formation sand intervals. This paper showcases an innovative workflow of using the petrophysical log attributes to characterize a heterogeneous reservoir sand by making use of ANN (Artificial Neural Net) and SMLP (Stratigraphic Modified Lorentz) based rock typing techniques as well as image based advanced sand layer computation techniques.
Vertical interference test is either performed using a wireline formation testing tool with multiple flow probes deployed in a vertical sequence at desired depth points on the borehole wall or using a drill stem test configuration. Based on the test design, flow rates are changed using downhole pumps, which induces pressure transients in the formation. The measured pressure response is then compared with a numerical model to derive the reservoir parameters such as vertical permeability, hydraulic connectivity etc. The conventional way of model generation is to consider a section of reservoir sand as homogenous, which generally leads to over estimation or underestimation of vertical permeabilities. The technique proposed in this paper utilizes advanced logs such as image logs; magnetic resonance logs, water saturation and other advanced lithology logs to obey heterogeneity in the reservoir model by utilizing ANN/SMLP based rock-typing techniques. These rock types would be helpful in making a multi layer formation model for the VIT modeling and regression approach. The vertical interference test model is then used to determine the vertical permeability values for each of the individual rock types. The paper displays the workflow to utilize the rock type based layered formation model in vertical interference test modeling for a channel sand scenario.
Vertical Interference Tests (VIT) using wireline formation testers are industry standard tests to estimate the vertical permeability of reservoir pay zones. In general, the test interval is considered homogeneous for the interpretation, leading to an inaccurate estimation of vertical permeability (Kv) in complex geological systems like thin laminated beds, channel sands, etc. This paper presents a novel approach of accounting for this heterogeneity through use of petrophysical and borehole image-based rock-typing methods, thus leading to a more realistic characterization of vertical permeability.
Advanced petrophysical logs and images are used to generate rock types through Artificial Neural Network (ANN) and Stratigraphic Modified Lorentz Plot (SMLP) techniques. These rock types are then used as an input into vertical interference test interpretation model, thereby factoring in the reservoir heterogeneity for deriving the vertical permeability. This is followed by a sensitivity analysis to examine the impact of the permeability results in multiple geological systems like channel sands, thin bed lamination, near to fault, pinch outs etc.
Vertical permeability (Kv) is a major input in majority of the advanced reservoir engineering calculations and has a significant impact on the field development plan and IOR/EOR techniques. This unique approach of accounting for rock types in a VIT interpretation model leads to a relatively good estimation of vertical permeability. The rock typing techniques used here, allow the user to define the number of layers and minimum interval thickness, which is extremely useful in highly laminated reservoirs. The sensitivity analysis plays a key role in understanding the utility limitations of both conventional and new approach in complex geological systems. In case of thick homogeneous reservoir, sand units, the conventional approach could be used with fairly accurate results. However, in cases of thin sand-shale units with low net-to-gross ratio, this approach gives a good estimation of layer-wise permeability distribution.
This paper presents a unique blend of petrophysical and dynamic workflows into a novel workflow. The results from the sensitivity study, discussed in the paper, can be used as standard criteria in determining the best suitable technique for interpretation of a vertical interference test. This unique approach allows the user to optimize on the interpretation time and to simultaneously ensure the accuracy of results.
Mukku, Vinil (Schlumberger) | Lama, Tshering (Oil India Limited) | Verma, Sanjay (Oil India Limited) | Kumar, Pankaj (Oil India Limited) | Bordeori, Krishna (Schlumberger) | Chatterjee, Chandreyi (Schlumberger) | Kumar, Arvind (Schlumberger) | Mishra, Siddharth (Schlumberger) | Sharma, Lovely (Schlumberger) | Batshas, Siddhanta (Schlumberger) | Shah, Arpit (Schlumberger) | Prasad, C. B. (Oil India Limited) | Pathak, Digantha (Oil India Limited) | Saikia, Partha Protim (Oil India Limited)
Hydraulic fracturing can establish well productivity in tight and unconventional reservoirs, accelerate production in low- to-medium permeability wells and revamp production in mature wells. However, not all wells are suitable candidates for hydraulic fracturing and the technique can be detrimental if the right candidate is not chosen. An integrated approach is required to select the wells that are the most-suitable candidates for hydraulic fracturing.
This paper discusses the hydraulic fracturing candidate selection workflow and execution carried out in the year 2015 to 2016, which has unlocked reservoir production potential of Upper Assam basin fields of Oil India Ltd. (OIL). Wells which showed poor/no inflow prior to hydraulic fracturing operations, exceeded operator expectations during post fracturing production. Better reservoir management through hydraulic fracturing, rejuvenated ceased wells with an incremental oil production rates of 1380 bopd cumulative rate from six wells, post fracturing. The candidate analysis workflow described in this paper, can serve as the best practices guide for any operator investigating workover candidates among multiple fields, with an objective of production enhancement.
A customized candidate selection methodology was developed to identify the 10 best candidates from a pool of 70 vertical/deviated wells in two phases of the hydraulic fracturing campaign. In the absence of dynamic reservoir analysis, offset well data analysis assisted in filling the data gaps by enabling geological and reservoir level understanding. Well production models were calibrated with the production history, geo-mechanical models were prepared and used in the fracture modelling to generate optimum fracture geometry and predict post-fracturing production. Wells were ranked according to incremental hydrocarbon production coupled with risk factors including completions integrity. In the execution, fracturing model was validated by performing fracturing diagnostics tests such as Step Rate and Minifrac injection. The final calibrated model was then used to design the optimum fracturing treatment. Given the age of wells and traditional completions architecture, best practices were developed to counter challenges of high pressures and rate limitations in wells with depth greater than 3500 m.
As stimulations and well preparation in completed wells are expensive, it was critical to identify the most-suitable candidates with the available dataset before attempting well preparation and further acquisition. This was addressed through a customized workflow to perform production rate transient analysis for reservoir dynamic flow properties, create synthetic geomechanical models for stress profile & fracture vertical growth estimation.
The Spraberry Trend oil field produces from a single enormous sand interbedded with shales, and typically pinch-out up dip. Being deposited in submarine channel systems and their associated fans, the sands are with very low porosity and permeability, both of which impede oil recovery. Oil has accumulated in stratigraphic traps, migrating upward from source rocks until find impermeable barriers. However, the natural fractures further complicated the hydrocarbon flow and pose drilling challenges in this field. A client has planned to drill deviated wells in Spraberry Trend area. They aimed to drill the wells with less drilling issues and Non-Productive Time. Wellbore instabilities and mud losses are more than in all wells drilled in Permian Basin and more specific in the prolific and acclaimed Midland Basin. Those drilling events are crucial to defining if the well will reach the goals and plans developed by geoscientists, drillers, and petroleum engineers. Decisions like casing points, open hole intervals, kick-off-points, curve sections, and finally landing points are very impacted if wellbore instabilities are present and this management is difficult to mitigate. To boost the drilling campaign, it was important to take control of the borehole problems and it was achieving through mud weight optimization. The laminated reservoir showed that isotropic stress model would not give correct stress parameters. Thus, a new workflow is being inherited to cogitate TIV (transverse isotropy vertical) stress model for wellbore stability analysis. TIV anisotropy analysis was performed on a representative vertical well using advanced acoustic measurements and the model was calibrated with post-drill events. This calibrated model gives the stable mud weight window for four planned laterals. This paper will highlight how a geomechanics-based approach, integrating advanced acoustic measurements, has significantly improved drilling rates by reducing drilling-related problems. The wells were drilled keeping the mud weight at the lower limit of the stable mud weight window. This led to faster drilling with an average rate of 1,300 ft/day. Reduction in wellbore instability issues has led to considerable reduction in time spent on reaming, mud circulation, cleaning tight spots, etc. This effectively reduced Non-Productive-Time and rig cost. Wellbore stability analysis incorporating anisotropic stress from advanced acoustic measurements has helped most right mud weight estimation and thereby drilling the wells at a faster rate in the unconventional reservoirs.
Reddy, Kondal (Cairn India Limited) | Gupta, Menal (Cairn India Limited) | McClenaghan, Ray (Cairn India Limited) | Saikia, Kausik (Cairn India Limited) | Mishra, Susanta (Cairn India Limited) | Rao, Challapalli (Cairn India Limited) | Joysula, Sivasankar (Cairn India Limited) | Kumar, Arvind (Cairn India Limited) | Shankar, Vivek (Cairn India Limited)
A 4D seismic survey was carefully planned, executed and interpreted on the Ravva Field. Geoscience and 4D seismic studies carried out on the field have provided key information that defines fault compartments, position of the current OWC and reveals potential undrained areas. The 4D seismic data has been used to optimize sub-surface targets, and underpinned Cairn India’s 2010-11 infill drilling campaign, which was instrumental in arresting the production decline in the field. The infill drilling results, as well as on-going dynamic reservoir surveillance programs are in line with 4D interpretations. All these results are being used to up-date the reservoir model for optimal reservoir management and development.
The 4D response in Ravva Field is result of a combination of changes in fluid saturation and pore pressures and hence requires discrete separation of the pressure and saturation components of the 4D effect to enable quantitative interpretation. A 4D simultaneous AVO inversion was carried out using base and monitor datasets to derive the quantifiable elastic property changes in the reservoir.
In this paper, we will first discuss the initial 4D interpretation results, the pitfalls of 4D interpretation based on AI – Vp/Vs domain and the importance of pressure and saturation decoupling from the 4D signal in order to identify bypassed oil areas. Next, we will discuss the methodology of decoupling of 4D signal using petro-elastic model and 4D inversion volumes and finally we will show the estimated saturation sections and maps portraying the water flooding signature in the flank side of the structure and undrained areas in the crestal portion.
Due to the increasing number of complicated problems and time consuminganalysis, the applications of advanced information technologies like fuzzyLogic, pattern recognition, intelligent networks and artificial neural networkhave gained momentum. Among all of them, Artificial Neural Network (ANN) provesto be having an edge on other computing applications for all types of datainterpretations and analysis work related to petroleum exploration as well asexploitation. Nowadays, ANN has been widely accepted as the most powerful andefficient tool especially for reservoir characterization. Reservoircharacterization mainly includes prediction of porosity, permeability,lithology, sand thickness, and well log data. This paper focuses on theapplication of ANN in the prediction of permeability and porosity of areservoir for a given well log data and seismic data. This paper discusses manyexamples which highlight the efficiency of ANN in obtaining nonlinear systemsand models for reservoir characterization problems. Well log data and seismicdata are the parameters which have been used in the prediction of porosity andpermeability using ANN in a carbonate reservoir.
Key Words: Reservoir Characterization, Artificial Intelligence, ArtificialNeural Network, Porosity, Permeability, Fluid Saturation, NeuronArchitecture.