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.
KS is a tight-sandstone and high-pressure-high-temperature (HPHT) gas reservoir in northwest China. It is characterized by a depth of more than 6000 m, temperature over 175°C, and pore pressure over 110 MPa. Despite the high unconfined compressive strength (UCS) of sandstone, almost half of the wells encountered sanding issues. The sanding wells exhibited low production rate, nozzle and pipeline erosion, sanding up, and even permanent closure. Investigating the sanding mechanism and developing solutions for sanding prevention are urgent needs due to the economic loss of low production.
An integrated sanding study was conducted to investigate the sanding mechanism. The entire sanding process was analyzed, including stress field alteration during production, rock failure, softening, and sand grain migration. First, wells with sanding issues were identified through production characteristics and field observation. After this, analysis of laboratory tests was performed to better understand the tight-sandstone properties, especially UCS, the softening parameter, and residual strength. Based on the tests, an elastoplastic damage model was proposed to delineate rock failure and sanding behavior. Then, a finite element model was built to simulate the damage of a perforation hole with field data, including hole diameter and length, rock stiffness and strength, drawdown, depletion, and so on. More simulation scenarios were performed to investigate the continuous sanding, transient sanding, and water hammer effect. Grain migration in perforation holes and in pipelines was also studied.
It was revealed that shear failure of perforation hole induced by drawdown and depletion was the root cause of sanding problem. Meanwhile, it was also confirmed that erosion and water hammer effect had very limited effect on sanding. Use of the elastoplastic damage model for the simulation of perforation hole failure enabled predicting the sand amount and determining the critical drawdown and depletion for sanding. In the end, an approach to identifying wells with high sanding risk and the key factors behind the sanding were provided, and sanding prevention suggestions were proposed.
The new elastoplastic damage model explains the sanding mechanism in a tight-sandstone reservoir and enables evaluating the sand volume, which has rarely been published previously. Laboratory tests, field observation, and numerical simulation were combined effectively to investigate the sanding issue. By utilizing the model, producers can find the key factors behind sanding issues, prevent sanding with a better production strategy, and avoid the economic loss, which are critical for the long-term exploration and production of this area.
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SPE is educating the next generation of aspiring engineers, scientists and managers about the oil and gas industry. This is an opportunity for school students in grades 9–12, studying Mathematics, Physics, Chemistry, Geography or interested in Petroleum Engineering are invited to join SPE members from all over the globe to discover the world of Petroleum Engineering. School teachers are invited to bring a group of 10–15 students. Students will be treated to a range of hands-on activities and presentations from renowned engineers. The oil price outlook coupled with the response of each oil and gas company to make ends meet has led to severe exploration budget cuts.
The basic objective of this course is to introduce the overview and concept of production optimisation, using nodal analysis as a tool in production optimisation and enhancement. The participants are exposed to the analysis of various elements that help in production system starting from reservoir to surface processing facilities and their effect on the performance of the total production system. Depth conversion of time interpretations is a basic skill set for interpreters. There is no single methodology that is optimal for all cases. Next, appropriate depth methods will be presented. Depth imaging should be considered an integral component of interpretation. If the results derived from depth imaging are intended to mitigate risk, the interpreter must actively guide the process.
Africa (Sub-Sahara) Vaalco Energy started oil production from the Etame 12-H development well offshore Gabon. The well was drilled to a measured depth of approximately 3450 m and was targeting the recently discovered lower lobe of the Gamba reservoir. It was brought on line at a rate of 2,000 BOPD with no indication of hydrogen sulfide. Vaalco (28.07%) is the operator with partners Addax Petroleum (31.63%), Sasol (27.75%), Asia Pacific KrisEnergy started drilling the Rossukon-2 exploration well on Block G6/48 in the Gulf of Thailand, using the Key Gibraltar jackup rig. The well will reach a total depth at 5,462 ft and will test Early Miocene stacked fluvial sandstones on a broad structural high.
Africa (Sub-Sahara) Bowleven has started drilling operations at the Moambe exploration well on the Bomono permit in Cameroon. Moambe is the second well in a two-well program, approximately 2 km east of the first well, Zingana. It targets a previously undrilled Paleocene Tertiary three-way dip fault block containing multiple sands and will be drilled to an estimated 1620 m in measured depth. Both wells will be logged. Bowleven is the operator and holds 100% interest. Asia Pacific Murphy Oil discovered gas at its Permai exploration well in deepwater Block H in the South China Sea offshore Malaysia.
Africa (Sub-Sahara) Equatorial Guinea's Ministry of Mines and Hydrocarbons has notified Ophir Energy that it will not gain an extension for the offshore Block R license. The block contains the deepwater Fortuna gas discovery. Ophir had been seeking to develop the gas using a Golar-converted floating liquefied-natural-gas (LNG) vessel, but failed to secure sufficient financial backing for the project. Front-end engineering design had begun in July 2015. Targeted production was approximately 330 MMcf/D, with a plateau of 30 years. Located approximately 140 km west of Bioko Island, the Fortuna project was to see development of six commercial discoveries in a phased manner.
Baker Hughes drilled one horizontal well for major Indian operating company in a, low resistivity contrast field, onshore India. The candidate field / basin is a proved petroliferous basin, located in the northeastern corner of India.
The scope of work for this project involved integrating geological and open hole offset parameters to build a Geosteering model. Integrated data included a study of offset well data from the field, regional and local dip analysis from wellbore images, and a review of structural maps. Successful integration of these data helped to steer the well in the desired zone as per plan and make the best use of the data and to reduce uncertainties in Geosteering, drilling. Although high-quality 16-sector images commonly yield bedding dip, fracture and other geological information, this paper emphasizes how real-time reservoir navigation decisions was made using Geosteering modelling, real-time image processing, dip picking study etc.