Pressure Measurements for Monitoring CO2 Foam Pilots

This study focuses on the use of pressure measurements to monitor the effectiveness of foam as a CO2 mobility control agent in oil-producing reservoirs. When it is applied optimally, foam has excellent potential to improve reservoir sweep efficiency, as well as CO2 utilization and storage, during CO2 Enhanced Oil Recovery (EOR) processes. In this study, we present part of an integrated and novel workflow involving laboratory measurements, reservoir modeling and monitoring. Using the recorded bottom-hole pressure data from a CO2 foam pilot study, we demonstrate how transient pressures could be used to monitor CO2 foam development inside the reservoir. Results from a recent CO2 foam pilot study in a heterogeneous carbonate field in Permian Basin, USA, are presented. The injection pressure was used to evaluate the development of foam during various foam injection cycles. A high-resolution radial simulator was utilized to study the effect of foam on well injectivity, as well as on CO2 mobility in the reservoir during the surfactant-alternating gas (SAG) process. Transient analysis indicated constant temperature behavior during all SAG cycles. On the other hand, differential pressures consistently increased during the surfactant injection and decreased during the subsequent CO2 injection periods. Pressure buildup during the periods of surfactant injection indicated the development of a reduced mobility zone in the reservoir. The radial model proved to be useful to assess the reservoir foam strength during this pilot study. Transient analysis revealed that the differential pressures during the SAG cycles were higher than the pressures observed during the water-alternating gas (WAG) cycle which, in turn, showed foam generation and reduced CO2 mobility in the reservoir. Although pressure data are a powerful indicator of foam strength, additional measurements may be required to describe the complex physics of in situ foam generation. In this pilot study, it appeared that the reservoir foam strength was weaker than that expected in the laboratory.publishedVersio

Was the M_w 7.5 1952 Kern County, California, earthquake induced (or triggered)?

Several recent studies have presented evidence that significant induced earthquakes occurred in a number of oil-producing regions during the early and mid-twentieth century related to either production or wastewater injection. We consider whether the 21 July 1952 M_w 7.5 Kern County earthquake might have been induced by production in the Wheeler Ridge oil field. The mainshock, which was not preceded by any significant foreshocks, occurred 98 days after the initial production of oil in Eocene strata at depths reaching 3 km, within ~1 km of the White Wolf fault (WWF). Based on this spatial and temporal proximity, we explore a potential causal relationship between the earthquake and oil production. While production would have normally be expected to have reduced pore pressure, inhibiting failure on the WWF, we present an analytical model based on industry stratigraphic data and best estimates of parameters whereby an impermeable splay fault adjacent to the main WWF could plausibly have blocked direct pore pressure effects, allowing the poroelastic stress change associated with production to destabilize the WWF, promoting initial failure. This proof-of-concept model can also account for the 98-day delay between the onset of production and the earthquake. While the earthquake clearly released stored tectonic stress, any initial perturbation on or near a major fault system can trigger a larger rupture. Our proposed mechanism provides an explanation for why significant earthquakes are not commonly induced by production in proximity to major faults

Was the M_w 7.5 1952 Kern County, California, earthquake induced (or triggered)?

Several recent studies have presented evidence that significant induced earthquakes occurred in a number of oil-producing regions during the early and mid-twentieth century related to either production or wastewater injection. We consider whether the 21 July 1952 M_w 7.5 Kern County earthquake might have been induced by production in the Wheeler Ridge oil field. The mainshock, which was not preceded by any significant foreshocks, occurred 98 days after the initial production of oil in Eocene strata at depths reaching 3 km, within ~1 km of the White Wolf fault (WWF). Based on this spatial and temporal proximity, we explore a potential causal relationship between the earthquake and oil production. While production would have normally be expected to have reduced pore pressure, inhibiting failure on the WWF, we present an analytical model based on industry stratigraphic data and best estimates of parameters whereby an impermeable splay fault adjacent to the main WWF could plausibly have blocked direct pore pressure effects, allowing the poroelastic stress change associated with production to destabilize the WWF, promoting initial failure. This proof-of-concept model can also account for the 98-day delay between the onset of production and the earthquake. While the earthquake clearly released stored tectonic stress, any initial perturbation on or near a major fault system can trigger a larger rupture. Our proposed mechanism provides an explanation for why significant earthquakes are not commonly induced by production in proximity to major faults

A Data-Driven Reduced-Order Model for Estimating the Stimulated Reservoir Volume (SRV)

The main goal of hydraulic fracturing stimulation in unconventional and tight reservoirs is to maximize hydrocarbon production by creating an efficient stimulated reservoir volume (SRV) around the horizontal wells. To zreach this goal, a physics-based model is typically used to design and optimize the hydraulic fracturing process before executing the job. However, two critical issues make this approach insufficient for achieving the mentioned goal. First, the physics-based models are based on several simplified assumptions and do not correctly represent the physics of unconventional reservoirs; hence, they often fail to match the observed SRVs in the field. Second, the success of the executed stimulation job is evaluated after it is completed in the field, leaving no room to modify some parameters such as proppant concentration in the middle of the job. To this end, this paper proposes data-driven and global sensitivity approaches to address these two issues. It introduces a novel workflow for estimating SRV in near real-time using some hydraulic fracturing parameters that can be inferred before or during the stimulation process. It also utilizes a robust global sensitivity framework known as the Sobol Method to rank the input parameters and create a reduced-order (mathematically simple) model for near real-time estimation of SRV (referred to as DSRV). The proposed framework in this paper has two main advantages and novelties. First, it is based on a pure data-based approach, with no simplified assumptions due to the use of a simulator for generating the training and test dataset, which is often the case in similar studies. Second, it treats SRV generation as a rock mechanics problem (rather than a reservoir engineering problem with fixed fracture lengths), accounting for changes in hydraulic fracture topology and SRV changes with time. A dataset from the Marcellus Shale Energy and Environment Laboratory (MSEEL) project is used. The model’s input parameters include stimulation variables of 58 stages of two wells. These parameters are stage number, step, pump rate and duration, proppant concentration and mass, and treating pressure. The model output consists of the corresponding microseismic (MS) cloud size at each step (i.e., time window) during the job. Based on the model, guidelines are provided to help operators design more efficient fracturing jobs for maximum recovery and to monitor the effectiveness of the hydraulic fracturing process. A few future improvements to this approach are also provided