Effective sand control is extremely important for production of hydrocarbons from shallow unconsolidated sand reservoir. In absence of any sand control measure installed in the well, there is high risk of Sand production along with the hydrocarbons. Sand production may result in damage of X-Mass tree and/or Sub-sea infrastructure components. In extreme cases, when the sand production goes beyond the critical limit, it may lead to a situation when the field production has to be stopped. In view of this, in Deep water environment, sand production is considered as a potential hazard. It may lead to tremendous impact on reservoir economics and sustained production from the field.

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Most static modeling workflows deal with stochastic simulations of the uncertain subsurface parameters on a base case model, although recent studies highlighted usefulness of discrete deterministic multiple geological scenario-based modeling. This paper illustrates the benefits of capturing the principal geological uncertainties through discrete subsurface scenarios, through a case study from the Vijaya and Vandana (V&V) field, Barmer Basin, northwest India. The 12 exploration and appraisal wells have established seven stratigraphically trapped oil pools with the maximum resources confined in the V&V mounds, consisting of turbidite sandstones and conglomerates in a shale background, inferred to be deposited in a deep lacustrine environment. Hydraulic fracturing of these sandstones resulted in significant production increase. Detailed subsurface analysis suggests that the V&V mounds consist of two channel complexes represented by a laterally migrating network of turbidite channels with a maximum thickness of 4–5 m for individual sandstones. Multiattribute seismic studies indicate that delineation of these channel sands, controlled mainly by the channel geometries, cannot be resolved by seismic signature/attribute studies alone, which necessitates the iteration of the facies model into five different scenarios. Each of the facies scenarios is further iterated with other key uncertain input parameters for STOIIP calculation (namely saturation, porosity, contact, etc.) to result in 50 deterministic static realizations that capture the wide uncertainty range of in-place volumes, through a cumulative distribution function plot. In the absence of a defined concept, our model highlights the importance of deterministic depiction of subsurface concepts (geologic, geophysical, petrophysical, and dynamic) through a scenario-based approach. This workflow captures a wide range of various high-impact uncertainties in an integrated manner and links discrete, deterministic, scenario-based outcomes to probabilistic reporting. This will help in the decision-making process by linking the model outcome with long-term well testing and ultimately the concept underlying the development plan.

Barmer Hill Turbidites (BHT) are low permeability reservoirs in the Vijaya & Vandana field with an approximate in place reserve of a billion barrels. The field was discovered in 2004 with the discovery wells V-1 and V-2 respectively. Post drilling and completion these wells were tested without any stimulation technique, resulting in ~ 25 – 50 BOPD flow owing to tight nature of these formations. Subsequently the zones were hydraulically fractured and tested resulting in ~ 10 – 12 folds increase in the production rate of the oil. Also, the testing of multiple stacked reservoirs in these two wells further confirmed BHT-10 to be the most prolific zone in terms of commercial flow rates achievable. Apart from being tight formations, the low net to gross on reservoirs (<20%) further added to the challenges of devising a strategy to make these reservoirs flow at sustained commercial oil rates. Hence, when the field was taken for the next stage of a hydrocarbon field lifecycle i.e. the appraisal campaign, two very clear objectives were identified for achieving a successful appraisal campaign viz. hydraulically frac and test two of the existing wells in the field while aiming to connect the maximum available KH and ensure effective data acquisition through injection tests and temperature logs with an aim to calibrate the existing stress logs and eventually build a robust frac model. The dynamic geo-mechanical parameters i.e. Young’s Modulus and Poisson’s Ration were calculated from the open hole sonic logs and were converted to static data using the lab measured value from the core tests. Stress logs generated from these static data points were used for the initial frac designing in the wells. During the execution phase of the frac campaign, at every opportunity available, injection tests were carried out and fall off data were acquired to estimate the closure pressures actually observed in these zones. Post acquiring the measured stress data, the earlier calculated stress logs were calibrated using these measured closure points (frac gradients) by incorporating the stress components due to strain factors (ɛmin & ɛmax) in both max and min direction of the principle stresses. Post every data injection, temperature logs were also acquired. This gave a better control on frac height (hydraulic height) based on the cool downs observed on the temperature logs. This proved to be a very important data set in comparing the height predicted by the calibrated stress logs versus the height estimated from the temperature log cool downs. This step helped in gaining confidence on the model predictability. This also helped in real time frac design optimization and placement of perforation intervals for the main frac designs. Further, the entire model calibration exercise also helped in arriving at a porosity based leak off equation. The paper endeavors to discuss in detail the entire workflow used during this appraisal campaign to arrive at a calibrated and a robust frac model whilst showcasing the journey taken from 50 BOPD to 500 BOPD in these tight oil sands to achieve ~ 10 fold production increase. Authors, further, emphasize on the importance of carrying out such data acquisitions during the appraisal phase of a field to gain better control on the models. This paper will also elaborate on the strategy deployed for these data acquisition to optimize the fracs in real time and to integrate different data sets for calibrating the geo-mechanical and frac simulation models.

In this article the title was incorrectly given as 'GERYA reservoir evolution model of synrift lacustrine hyperpycnites, Barmer Basin (Rajasthan, India)' but should have been 'A reservoir evolution model of synrift lacustrine hyperpycnites, Barmer Basin (Rajasthan, India)'.The original article has been corrected.