Jia, Ying (Petroleum Exploration and Production Research Institute, SINOPEC) | Shi, Yunqing (Petroleum Exploration and Production Research Institute, SINOPEC) | Huang, Lei (Research Institute of Petroleum Exploration and Development, Petrochina) | Yan, Jin (Petroleum Exploration and Production Research Institute, SINOPEC) | Sun, Lei (SouthWest Petroleum University)
The YKL condensate gas reservoir is one of the biggest condensate gas reservoirs in China and has been developed more than 10years. At present, the combination of subdivision layer, production speed optimization and horizontal well drilling has been the key to economically unlocking the vast reserves of the YKL condensate gas. The primary recovery factor, however, remains rather low due to high capillary trapping and water invasion. While primary depletion could result in low gas recovery, CO2 flooding provides a promising option for increasing the recovery factor.
The objective of this work is to verify and evaluate the effect supercritical CO2 on enhancing gas recovery and analyze the feasibility of CO2 enhance gas recovery (CO2 EGR) of condensate gas reservoir.
Firstly, novel phase behavior experimental procedures and phase equilibrium evaluation methodology for gas-condensate phase system mixed with supercritical CO2 with high temperature were presented. A unique phase behavior phenomena was also reported. Then, CO2 floodingmechanism in condensate gas reservoir was analyzed and clarified based on experiments. Finally, a series of numerical simulation work were conducted as an effective and economical means to maximize natural gas recovery with the lowest CO2 breakthrough by varying strategies, including CO2 injection rate, injection composition, andinjection timing. Meanwhile the CO2 storage volumes of different strategies were calculated.
The results show that higher gas recovery factor can be achieved with CO2 injection through appearing interphase between two fluids, maintaining reservoir pressure, driving gas like "cushion" and controlling water invasion. All strategies have moderate to significant effects on gas production. The control of injection and production ratio needs to be balanced between pressure transient and CO2 breakthrough over the producer to obtain the maximum gas production. The varying injection pressure shows a positive effect of enhancing gas production. Numerical simulation indicated that the recovery of gas reservoir was improved by around 10 percent. The total CO2 storage would be around 30-40% HCPV.
The research showed that CO2 flooding presents a technically promising method for recovering the vast condensate gas while extensively reducing greenhouse gas emissions.
Recent studies have indicated that Huff-n-Puff (HNP) gas injection has the potential to recover an additional 30-70% oil from multi-fractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution), and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example.
Compositional simulations are conducted which incorporate a tuned PVT model and a set of measured cyclic injection/compaction pressure-sensitive permeability data. Markov chain Monte Carlo (McMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The McMC process is accelerated by employing an accurate proxy model (kriging) which is updated using a highly adaptive sampling algorithm. Gaussian Processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ-σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions.
The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half-length, are narrower, while wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of around 1.5 months, a production time of around 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubble point are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production.
The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced machine learning techniques for optimization under uncertainty, resulting in better decision making.
The SWP project is located in a mature waterflood undergoing conversion to CO2-WAG operations at Farnsworth, Texas, USA. Utilized CO2 is anthropogenic, sourced from a fertilizer and an ethanol plant. Major project goals are optimizing the storage/production balance, ensuring storage permanence, and developing best practices for CCUS.
This paper provides a review of work performed toward development of a 3D coupled Mechanical Earth Model (MEM) for use in assessment of caprock integrity, fault reactivation potential, and evaluation of stress dependent permeability in reservoir forecasting. Mechanical property estimates computed from geophysical logs at selected wellbores were integrated with 3D seismic elastic inversion products to create a 3D "static" mechanical property model sharing the same geological framework as the existing reservoir simulation model including 3 major faults. Stresses in the MEM were initialized from wellbore stress estimates and reservoir simulation pore pressures. One way and two way coupled simulations were performed using a compositional hydrodynamic flow model and geomechanical solvers.
Coupled simulations were performed on history matched primary, secondary (waterflood), and tertiary (CO2 WAG) recovery periods, as well as an optimized WAG prediction period. These simulations suggest that the field has been operating at conditions which are not conducive to either caprock failure or fault reactivation. Two way coupled simulations were performed in which permeability was periodically updated as a function of volumetric strain using the Kozeny-Carmen porosity-permeability relationship. These simulations illustrate the importance of frequent permeability updating when recovery scenarios result in large pressure changes such as in field re-pressurization through waterflood after a long primary depletion recovery period. Conversely, production forecasting results are less sensitive to permeability update frequency when pressure cycles are short and shallow as in WAG cycles.
This paper describes initial work on development of a mechanical earth model for use in assessment of geomechanical risks associated with CCUS operations at FWU. The emphasis of this work is on integration of available geomechanical data for creation of the static mechanical property model. Preliminary coupled hydro-mechanical simulations are presented to illustrate some of the key diagnostic output from coupled simulations which will be used in later work for in depth evaluation of specific risk factors such as induced seismicity and caprock integrity.
Hadi, Farqad (Petroleum Engineering Department, Baghdad University) | Albehadili, Ali (Iraqi Drilling Company) | Jassim, Abduihussein (Najaf Oil Fields) | Almahdawi, Faleh (Petroleum Engineering Department, Baghdad University)
Formulating a prediction tool that can estimate the formation permeability in uncored wells is of particular importance for many applications related to reservoir simulation and production management. Although formation permeability can be obtained from a laboratory or from a reservoir, core analysis and well-test data are limited due to cost and time-saving purposes. A major challenge of previous methods is that they are required other parameters to be previously computed such as porosity and water saturation. In addition, they are affected by the uncertainty that introduced by the cementation factor and saturation exponent. This study presents two prediction methods, multiple regression analysis (MRA) and artificial neural networks (ANNs), to estimate formation permeability using conventional well log data.
The prediction methods were demonstrated by means of a field case in SE Iraq. The study uses core/well log data from Mishrif reservoir which is mainly composed of carbonate (limestone) formations. Two traditional methods were reviewed and presented for permeability determination. These methods are the classical method and the flow zone indicator (FZI) method.
At the same porosity, the results showed a wide range of formation permeability prediction. This result gives a special attention to the assumption that the relationship between permeability and porosity is generally unique in carbonate environments. The deep lateral log resistivity appears to be more conservative in the permeability function rather than other parameters, followed in decreasing order by bulk density, sonic travel time, micro and shallow resistivities, and shale volume. Although the presented models based on RA and ANNs resemble to be closely in determining the formation permeability, the correlation coefficient of ANNs was found to be higher than that obtained from RA, which indicated that the ANNs is more precise than RA. The comparison among previous methods shows the superiority of the FZI method rather than the classical method. However, core porosity and permeability should be previously determined to apply FZI method. This study presents efficient and cost-effective models for a prediction of permeability in uncored wells by incorporating conventional well logs.
Africa (Sub-Sahara) Algeria awarded four of 31 oil and gas field blocks on offer to foreign consortiums in its first auction since 2011. Shell and Repsol won permits for the Boughezoul area in the north of the country, while Shell and Statoil won permits for the Timissit area in the east. A consortium of Enel and Dragon Oil was awarded permits for both the Tinrhert and the Msari Akabli areas. Circle Oil's CGD-12 well, located onshore Morocco in the Sebou permit, encountered natural gas at different levels within the Guebbas and Hoot sands. Wireline logging analysis confirmed a net 9.7 m of pay. The first test, over the Intra Hoot sands, flowed gas at a sustained rate of 2.21 MMscf/D through an 18/64‑in. The primary target, the Main Hoot sands, flowed at a sustained rate of 4.62 MMscf/D through a 24/64-in.
Africa (Sub-Sahara) ExxonMobil subsidiary Esso Exploration Angola has started oil production at the Kizomba Satellites Phase 2 project offshore Angola. The project involves the development of subsea infrastructure for the Kakocha, Bavuca, and Mondo South fields. Mondo South is the first field to begin production, and the other two satellite fields will follow later this year. The goal is to increase Block 15's production to 350,000 BOPD. Esso (40%) is the operator with BP Exploration Angola (26.67%), Kosmos Energy discovered gas at the Tortue West prospect in Block C-8 offshore Mauritania.
Africa (Sub-Sahara) Kosmos Energy has made a significant deepwater gas discovery off Senegal. The Guembeul-1 well in the northern part of the St. Louis Offshore Profond license in 8,858 ft of water encountered 331 net ft of gas pay in two excellent-quality reservoirs, the company reported. The results demonstrate reservoir continuity and static pressure communication with the Tortue-1 well, which suggests a single gas accumulation. The mean gross resource estimate for the Greater Tortue complex has risen to 17 Tcf from 14 Tcf as a result of the Guembeul discovery, the company said. Kosmos, the operator, has a 60% interest in the well. Timis (30%) and Petrosen (10%) hold the remaining interest. In Salah Gas has started production from its Southern fields in Algeria.
Africa (Sub-Sahara) Petroceltic International said that the first of up to 24 new development wells planned in Algeria's Ain Tsila gas and condensate field was successful. The AT-10 well, situated about 2 miles from the AT-1 field discovery well, reached a total depth of 6,578 ft. Wireline logs indicated that the expected initial offtake rate would be comparable to the AT-1 and AT-8 wells, both of which test-flowed at more than 30 MMcf/D. Petroceltic is the operator with a 38.25% interest in the production-sharing contract that covers the Ain Tsila output. The remaining interests are held by Sonatrach (43.375%) and Enel (18.375%). Sonangol reported that it has found reserves in the Kwanza Basin of Angola that could total 2.2 billion BOE, including reserves in a block jointly owned with BP. Block 24, operated by BP, holds an estimated 280 million bbl of condensate and 8 Tcf of gas, totaling 1.7 billion BOE, Sonangol said in a statement seen by Reuters.
Africa (Sub-Sahara) Sound Energy identified significant gas shows at the first Tendrara well onshore Morocco. Drilled to a 2665-m measured vertical depth, the well hit a total gross pay interval of 89 m of gas. The full set of well logs are being processed before the startup of rigless mechanical reservoir stimulation operations, which will be followed by a well test. The company is the operator of the well with a 55% working interest.