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The goal of the present work is to evaluate ultra light weight proppants for fracturing of shale gas reservoirs. Three ultra light weight proppants (ULW-1, ULW-2, and ULW-3) have been studied. The mechanical properties of the proppant packs as well as single proppants have been measured. Conductivity of proppant packs has been determined as a function of proppant concentration, confining stress, and temperature. The crush strengths of all the three proppant packs are higher than typical stresses encountered in the Barnett shale. ULW-1 and ULW-2 are highly deformable and do not produce many fines. ULW-3 has a higher Young's modulus and produces fines. The proppant conductivity decreases with increasing confining stress, but it is not a strong function of the proppant concentration for bothe ULW-1 and ULW-2. A partial monolayer of ULW-2 (0.07 lbm/ft2) provides slightly less or as much conductivity as a thick proppant pack with a concentration of 0.7 lbm/ft2. At 6000 psi stress, the conductivity of ULW-1 seems to decrease a little as the proppant concentration increases and then increases. Application of light weight proppants in stimulation treatments in shale reservoirs can lead to long propped fractures, which can improve the productivity of fractured shale reservoirs.

The objective of this work is to accurately determine horizontal shale well EUR for an area integrating geology, machine learning, pattern recognition and statistical analysis using various parameters of nearby producing horizontal shale wells, as inputs. This work utilizes local geological information followed by execution of machine learning to identify critical well parameters that lead to better production. Then a pattern recognition step is performed while making sure the number of wells in each category are statistically significant. This also serves as a quality control measure by not basing the conclusion solely on the results of machine learning. The conclusions are verified using available literature on correlation between well production and various well parameters. Wells with optimum (controllable) parameters are selected to obtain a type curve for the target zone(s) in the area of interest. The above-mentioned methodology helped in making the type-curve/EUR determination process scientific, systematic and seamless. Machine learning helped in identifying the key well-parameters that correlate to better production. Visual pattern recognition strengthened the confidence in the relationships identified in the last step. Different parameters were shown to affect production in different areas/targets confirming that every shale asset requires a thorough research before reaching a reasonable conclusion. The type-curves were established for each Wolfcamp bench in the area of interest selecting wells with optimum completions. The optimum completions parameters were identified by the methodology prescribed in the paper. This assisted in identifying the area of interest's true economic potential with regards to horizontal shale well development. This paper prescribes a novel scientific data-intensive methodology to systemically use well data in a step-wise manner to identify the type-curve and EUR/well for an area, thereby determining the area's true economic potential. Along with the prescribed big-data mining methodology, the most important take away from this study is: for the optimum evaluation of shale assets it is critical to tie in the controllable well parameters to well production. Once this relationship is established, the type-curve determination and the EUR estimation can be done more accurately.

The objective is to establish a robust methodology for evaluating the true economic potential of an asset integrating various sources of data, namely, geological, completions, production and leasing data retrieved from public sources. A three segment methodology has been adopted in assessing properties for acquisition or farmout opportunities. The central idea of the work is that completions optimization is an ever-changing process. This implies the areas that were previously overlooked could potentially benefit from recent advances in completions and be more profitable. Data mining integrated with available geological information assists in identifying the key parameters that affect well performance. Once optimized for completions parameters, one can identify the real potential of an asset under consideration. When geological factors, completions parameters, normalized production type curves, cost of drilling, completions and leasing are taken into account, we reach an even ground comparing different assets. Using this methodology, opportunities were identified in different areas that were previously overlooked. Some of the geological factors that should always be taken into account are depth (TVD), reservoir pressure, existing PVT/GOR studies, and prior knowledge of faults/fractures. The completions parameters that influence production have a wide range. However, it is reasonable to assume that length of lateral, stage spacing, type of fluid, proppant amount, rate of pumping and clusters per stage are the more important ones. The proppant amount, fluid type and rate of pumping can be inter-dependent. In areas with a significant number of horizontal wells on production, data on completions, geology and production is usually available on Public Sources, like the Texas Railroad Commission. The aggregation of data on several wells feeds into the creation of a huge data-set, in other words, big data. Once a combination of above mentioned factors is taken into account, the pattern identification process related to correlation of performance vs. parameters is quickly established. The type curve which corresponds to the optimum parameters for the particular area of interest, is then used for economic forecasting. This paper truly integrates various factors that go into investment decision making. The advances in data visualization tools and the availability of big data from public sources assist in solidifying this robust methodology. The methodology described can be utilized by E&P companies, investment banks and private equity groups to make well informed investment decisions in the future.

The objective of this work is to explain the deviation in GOR/GLR (Gas-Oil-Ratio/Gas-Liquid-Ratio) of hydraulically fractured Wolfcamp horizontal shale well(s) from the type-curve GOR/GLR in the gascondensate region of the Permian Basin. We relied on the detailed field data reported during the flow-back period of a recently completed well. We identified various aspects of the casing-tubing design, operational changes that were made and any concurrent production disturbance that were noticed. Then we did a thorough literature review of GOR/GLR behavior in gas condensate wells. We identified the key concepts that were applicable to the well in our study. We applied the existing equations for the critical gas rate required to lift liquids in gas wells and highlighted the variation in gas velocity with changing pressure and temperature along the vertical part of the wellbore. Using this approach, we demonstrated the relationship between changing liquid fraction along the vertical part of the wellbore with the changing gas velocity. A wide range of GOR/GLR was noticed for the well in our study over a period of only a few days of flow- back. The GOR/GLR curves from the nearby producing wolfcamp wells and the PVT diagram available from a nearby wolfcamp well could not explain the observed deviations in GOR/GLR. However, there were some key changes made in the production methodology during the flow-back period. We identified the appropriate mechanisms of multi-phase flow applicable to each operational change during flow back. After identifying the appropriate multi-phase flow back mechanism, we implemented the concept of critical gas velocity (flow-rate) along the wellbore. This enabled us in explaining the GOR/GLRs observed in each operational regime. The corrected GOR/GLR of the well in study aligned with the GOR/GLR of the type-curve of horizontal shale wells obtained from the nearby hydraulically fractured horizontal wells targeting the Wolfcamp in the same formation. This is a unique study that utilizes the concept of critical flow rate, the different mechanisms of multiphase flow in the production tubing and annulus in identifying the advent of liquid loading. This work assists in explaining the discrepancies observed in the GOR/GLRs in the early production history of horizontal gas wells in gas-condensate regions. In the past, the concept of liquid-loading and the proposed remedies have been applied late in the life of gas wells with small gas rates to explain the abnormaility of the the GOR/GLR. This paper shows that the observed GOR/GLRs on the surface is not solely a function of the PVT diagram. Finally, integrating the learnings from this well with the liquid-loading concepts, we make recommendations for improved completions and flow back methodology for future wolfcamp horizontal gas-condensate wells in the Permian Basin.