Swami, Vivek (CGG) | Tavares, Julio (CGG) | Pandey, Vishnu (CGG) | Nekrasova, Tatyana (CGG) | Cook, Dan (Bravo Natural Resources) | Moncayo, Jose (Bravo Natural Resources) | Yale, David (Yale Geomechanics Consulting)
In this study, a state-of-the-art seismic driven 3D geological model was built and calibrated to a petrophysical and geomechanical analysis, 1D-MEM (Mechanical Earth Model), on chosen wells within the Arkoma Basin of Oklahoma. The well information utilized in this study included basic wireline logs and core analysis, including XRD (X-Ray diffraction) data. The traditional petrophysical analysis was augmented with advanced rock physics and statistical techniques to generate the necessary logs. Hydrostatic, overburden and pore pressures were calculated with a petrophysical evaluation model. The 1D-MEMs were based on the Eaton/Olson/Blanton approach with the HTI (Horizontal Transverse Anisotropy) assumption. The 1D-MEMs were calibrated to laboratory data (triaxial tests) and field observations (mud logs, wellbore failure, frac pressures). Therefore, a very good confidence was achieved on Biot's coefficient, tectonic components, anisotropy and dynamic to static conversion factors for Young's Modulus and Poisson's Ratio. Seismic inversions were performed in different time windows and merged to generate high resolution P- and S-Impedance attributes from surface down to the target interval after careful AVO compliant gather preconditioning. A density volume estimate was calibrated to well data, accounting for different geological formations, to decouple P- and S-Wave components as a 3D volume, as well as dynamic Young's modulus (E) and Poisson's ratio (PR). Dynamic E and PR were converted to static parameters using results from 1D-MEMs; and 3D models of Biot's coefficient (α) and tectonic components were built to compute 3D fracture pressure volumes calibrated to well data. The final products were seismic-driven 3D pore pressure and fracture pressure calibrated to 1D-MEMs. The correlation between measured/estimated well logs and corresponding seismic-derived pseudo logs was more than 80%, which indicates good quality of seismic inversion results and hence 3D-MEM. Also, stress barriers, anisotropy, and brittleness indices were calculated on well scale which would help to identify best zones to place hydraulic fractures. The 3D geological model will aid in identifying sweet-spots and optimizing hydraulic fractures.
Copyright 2018, Unconventional Resources Technology Conference (URTeC) This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Houston, Texas, USA, 23-25 July 2018. The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted by the author(s). The contents of this paper have not been reviewed by URTeC and URTeC does not warrant the accuracy, reliability, or timeliness of any information herein. All information is the responsibility of, and, is subject to corrections by the author(s). Any person or entity that relies on any information obtained from this paper does so at their own risk. The information herein does not necessarily reflect any position of URTeC. Any reproduction, distribution, or storage of any part of this paper by anyone other than the author without the written consent of URTeC is prohibited. Abstract Standard seismic/ acoustic log Pp prediction techniques developed for young sediments in offshore basins are not very effective in unconventional reservoirs. The age and lithification of shale reservoirs, the variability in lithology, and different overpressure generation mechanisms and basin histories all lead to poor quality predictions using standard Eaton or Bowers methods. But Pp prediction remains important in unconventional reservoirs due to the correlation between overpressured areas and productivity, and the correlations between thermal maturity and pore pressure. We have developed a method that extends the theoretical basis of the Eaton and Bowers methods to the geologic and basin history conditions of unconventional reservoirs. The method has been developed using standard log suite along with dipole acoustic logs.
Swami, Vivek (CGG Services Canada Inc.) | Settari, Antonin (CGG Services Canada Inc.) | Sahai, Raki (Chesapeake Energy Corp.) | Costello, Dan (Chesapeake Energy Corp.) | Mercer, Ashley (Chesapeake Energy Corp.)
Many operators have used in the past various methods to analyze and optimize the horizontal well (HW) completions in the Eagle Ford play with varied results. Typically, such methods focus on different parts of this complex problem in relative isolation and as a consequence do not utilize all available data simultaneously. This paper presents a simulation-based method for analyzing the problem in an integrated fashion by modeling the fracturing and Stimulated Reservoir Volume (SRV) creation process, followed by well cleanup and production. Consequently, all available data are used to constrain the history match (HM), resulting in a more reliable tool for optimization.
In this work, the authors developed a comprehensive integrated model of a typical Eagle Ford well in the Dimmit County. The HM process showed that injection and production scenarios must be modeled in tandem to get better insights into the flow physics rather than simulating them separately. The best accuracy is obtained when the real sequence of fracturing is modeled. It was found that only a fraction of the created fracture and SRV lengths contribute to production. Whereas fracture half-lengths of ~250 ft were generated during injection, only about ¼ of fracture and ¾ of SRV contributed. Effect of completion efficiency was also investigated. It was shown that the assumption of only 2 perforation clusters per stage is not plausible while assuming some other scenarios offers good HM and prediction very similar to uniform efficiency.
Optimization work considered several scenarios. Cases with larger cluster/stage spacing with the same pumped volume are not desirable. However, the use of double the cluster spacing gives slightly higher estimated ultimate recovery in 30 years, and could offer significant completion cost savings. Use of current injection volumes and current well spacing (500 ft) leaves significant reservoir volume undrained, which is a target for well spacing optimization. Pressure (as opposed to stress) dependent permeability functions adequately capture the permeability variation both for injection and production.
The work shows how the integrated reservoir/fracturing/geomechanics modeling can be used to optimize completions and EUR for shale wells.
Shale is a complex unconventional reservoir having a variety of storage and flow mechanisms coupled together. Contrary to conventional reservoirs where gas is stored only in the pore space as free gas; current numerical simulators assume gas is stored as free and adsorbed phase in shale. Recent advancement of visualization and measurement techniques has enabled us to look at shale more closely. Shale has been found to contain a well-developed nanopore network in the organic matter or kerogen. Correspondingly we believe that gas is stored via four storage mechanisms: gas in natural fractures, free gas in matrix pores, adsorbed gas and gas dissolved in kerogen bulk in the shales.
In this work we formulate a flow model for shale gas reservoirs including the physics at nano scale. The model incorporates the gas stored in micro-fractures, gas stored in nanopores, gas adsorbed on the pore walls and gas dissolved in kerogen bulk. This complex quad porosity system has coupled equations between three interconnected systems; between matrix and fracture set, between matrix and adsorbed gas and between matrix and kerogen bulk. Sets of governing equations were derived for the coupled systems and numerically solved to find gas production as a function of time. The model was validated against laboratory observed data for a shale canister test from a Canadian shale gas field. This laboratory scale model can be suitably up-scaled for field scale simulation of shale reservoirs.
Shale reservoirs are quite different from conventional reservoirs with respect to deposition history, storage mechanisms and transport behavior (caused by contrast in rock pore size). First—whereas in a conventional reservoir the hydrocarbons migrate from source rock to reservoir rock, shale is both the source and reservoir rock in itself. The organic matter gets deposited and converted into gas but does not escape. This is a result of the extremely low permeability of shale pores and the absence of large pressure gradients. Second—in conventional reservoirs, hydrocarbon is stored only in the pore spaces but there are multiple storage mechanisms active in shale owing to its complex composition. Current belief is that the gas associated with shale gas reservoirs is stored as free gas (natural fractures and nanopores) and adsorbed only (Boyer et al., 2006). Shale is a complex mixture of organic matter (kerogen), inorganic matter (clay, silica) and interbedded minerals (feldspar etc.). The organic matter is found to contain a well-developed network of nanopores. As can be seen in Fig. 1 (SEM image from a Barnett shale sample) almost all the nanopores are only located inside the kerogen. It can be deduced that almost entire porous space occurs inside kerogen only. Hashmy et al. (2011) claim that the porosity inside the kerogen can reach up to 40%. Kerogen can be further subdivided into two parts; porous space (nanopores) and kerogen bulk (solid). Traditionally kerogen bulk has not been considered a very significant gas holder. Assuming kerogen bulk's equivalence with bitumen, it would have appreciable gas solubility. Kerogen fraction could reach up to 40% v/v compared to total shale bulk volume in certain organic rich shales (Passey et al., 2011). Therefore kerogen can store a significant amount of gas in the dissolved phase. Also, the rate of diffusion from kerogen bulk to shale pores has historically been considered negligible (Hashmy et al., 2011). In our previous work (Swami & Settari, 2012) we showed that it is very significant theoretically and in fact can affect the pressure transients and cumulative recovery behavior. The total gas in place should consist of free gas stored in the natural fractures (if present), the nanopores, adsorbed gas on the organic matter surface and gas dissolved inside the kerogen bulk.
Many shale gas and ultra-low permeability tight gas reservoirs can have matrix permeability values in the range of tens to hundreds of nanodarcies. The ultra-fine pore structure of these rocks can cause violation of the basic assumptions behind usage of Darcy's law. Depending on a combination of P-T conditions, pore structure and gas properties, non-Darcy flow mechanisms such as Knudsen diffusion and/or gas slippage effects could be important. Quantifying these effects is critical for correcting laboratory permeability measurements to obtain true (intrinsic) matrix permeability; several authors have also noted that corrections for these effects may also be important when analyzing field data. In order to make corrections for non-Darcy flow, numerous authors have quantified these effects as an apparent permeability that changes as a function of Knudsen number or gas pressure. There are now many correlations available for quantifying apparent permeability changes, but it is not known how much impact they really have on the long term production performance of shale gas wells.
In this work, we summarize the various methods for quantifying non-Darcy flow in unconventional gas reservoirs, and compare the apparent permeability and slippage factor predictions for all the models we have compiled. For determining the impact of the model predictions on well performance, we incorporate the apparent permeability predictions of each model into a numerical simulator, and compare rate-time and cumulative gas-time forecasts for each scenario. The importance of dual porosity (which is usually assumed necessary for shale gas) is also examined. The results of this work are important in several aspects. First, the differences between various formulations of the apparent matrix permeability in shales are illustrated. Secondly, the impact of non-Darcy effects on history matching and recovery forecasting is demonstrated. Finally, it is shown that there may not be a need for dual porosity modeling in certain cases.
The results of this study will be important to professionals involved in laboratory measurement of matrix permeability in unconventional gas reservoirs, modeling well performance, and forecasting shale gas recovery.
It has been observed over the years that shale gas production modeled with conventional simulators/models is much lower than the actually observed field data. Generally reservoir and/or stimulated reservoir volume (SRV) parameters are modified (without much physical support) to match the production data. Instead of modifying the reservoir parameters without physical support, we aim to investigate the shale closely and see if we are missing some vital part in the flow physics.
Shale is a complex unconventional reservoir with a significant organic fraction. Traditionally, it is perceived that the gas is stored in pore space and adsorbed on pore surfaces. In this work, we postulate that significant amount of gas is also stored in the bulk of organic matter or kerogen. We show a conceptual model of one shale pore and model the flow behavior taking into account the free gas (stored in natural fractures and nanopores), adsorbed gas, and gas dissolved in kerogen. Therafter, we upscale the model to a laboratory scale sample. We propose a numerical model for the complex "quad?? porosity system while also accounting for non Darcy flow in shale nanopores. We then calibrate the model against a laboratory experimental data. This laboratory scale model can be upscaled suitably for field scale simulation of shale reservoirs.
It has been observed that the shale gas production modeled with conventional simulators/models is much lower than actually observed field data. Generally reservoir and/or stimulated reservoir volume (SRV) parameters are modified (without much physical support) to match production data. One of the important parameters controlling flow is the effective permeability of the intact shale. In this project we aim to model flow in shale nano pores by capturing the physics behind the actual process. For the flow dynamics, in addition to Darcy flow, the effects of slippage at the boundary of pores and Knudsen diffusion have been included. For the gas source, the compressed gas stored in pore spaces, gas adsorbed at pore walls and gas diffusing from the kerogen have been considered. To imitate the actual scenario, real gas has been considered to model the flow. Partial differential equations were derived capturing the physics and finite difference method was used to solve the coupled differential equations numerically. The contribution of Knudsen diffusion and gas slippage, gas desorption and gas diffusion from kerogen to total production was studied in detail. It was seen that including the additional physics causes significant differences in pressure gradients and increases cumulative production. We conclude that the above effects should be considered while modeling and making production forecasts for shale gas reservoirs.