This paper discusses the re-construction of the long-term development plan for an offshore giantfield located in Abu Dhabi with the aim to mitigate the rising challenges in the maturing field. The primary objective is to understand the reservoir behavior in terms of fluid movement incorporating the learning from the vast history while correlating with the geological features.
The field has been divided into segments based on multiple factors considering the static properties such as facies distribution, diagenesis, faults, and fractures while incorporating the dynamic behaviors including pressure trends and fluid movements.
On further analysis, various trends have been identified relating these static and dynamic behaviors. The production mechanism for each of the reservoirs and the subsequent sub reservoirs were analyzed with the help of Chan plots, Hall plots and Lorentz plots which distinctly revealed trends that further helped to classify the wells into different production categories.
Using the above methodology the field has been categorized in segments and color coded to indicate areas of different ranking. The green zone indicates area of best interest which currently has strong pressure support and wells can be planned immediately. The wells in this area are expected to produce with a low risk of water and gas. The yellow zone indicates areas of caution where special wells including smart wells maybe required to sustain production. This area showed relatively lower pressure support owing the location of the water injectors and the degraded facies quality between the injectors and the producers. The red zone highlights areas which are relatively mature compared to the neighboring zones and will require new development philosophy to improve the recovery. The findings from this study were used as the basis for a reservoir simulation study using a history matched model, to plan future activities and improve the field recovery.
This study involved an in-depth analysis incorporating the latest findings with respect to the static and dynamic properties of the reservoir. This has helped to classify the reservoir based on the development needs and will play a critical role in designing the future strategies in less time.
Taha, Taha (Emerson Automation Solutions) | Ward, Paul (Emerson Automation Solutions) | Peacock, Gavin (Emerson Automation Solutions) | Heritage, John (Emerson Automation Solutions) | Bordas, Rafel (Emerson Automation Solutions) | Aslam, Usman (Emerson Automation Solutions) | Walsh, Steve (Emerson Automation Solutions) | Hammersley, Richard (Emerson Automation Solutions) | Gringarten, Emmanuel (Emerson Automation Solutions)
This paper presents a case study in 4D seismic history matching using an automated, ensemble-based workflow that tightly integrates the static and dynamic domains. Subsurface uncertainties, captured at every stage of the interpretative and modelling process, are used as inputs within a repeatable workflow. By adjusting these inputs, an ensemble of models is created, and their likelihoods constrained by observations within an iterative loop. The result is multiple realizations of calibrated models that are consistent with the underlying geology, the observed production data, the seismic signature of the reservoir and its fluids. It is effectively a digital twin of the reservoir with an improved predictive ability that provides a realistic assessment of uncertainty associated with production forecasts.
The example used in this study is a synthetic 3D model mimicking a real North Sea field. Data assimilation is conducted using an Ensemble Smoother with multiple data assimilations (ES-MDA). This paper has a significant focus on seismic data, with the corresponding result vector generated via a petro-elastic model. 4D seismic data proves to be a key additional source of measurement data with a unique volumetric distribution creating a coherent predictive model. This allows recovery of the underlying geological features and more accurately models the uncertainty in predicted production than was possible by matching production data alone.
A significant advantage of this approach is the ability to utilize simultaneously multiple types of measurement data including production, RFT, PLT and 4D seismic. Newly acquired observations can be rapidly accommodated which is often critical as the value of most interventions is reduced by delay.
In addition to knowing the values of in-situ stress, it is also extremely important to know the values of formation permeability in every rock layer. It is impossible to optimize the location of the perforations, the length of the hydraulic fracture, the conductivity of the hydraulic fracture, and the well spacing, if one does not know the values of formation permeability in every rock layer. In addition, one must know the formation permeability to forecast gas reserves and to analyze post-fracture pressure buildup tests. To determine the values of formation permeability, one can use data from logs, cores, production tests, and prefracture pressure buildup tests or injection falloff tests. The most data that are available vs. depth comes from openhole logs.
There are two options for the dlim value: "dlimexponential" and "dlimhyperbolic". When using the "dlimexponential", the decline will transition such that the exponential portion of the decline will have an effective decline rate of the dlimvalue specified. When using the "dlimhyperbolic", the decline will transition when the hyperbolic portion reaches the specified dlim value. The exponential portion will then have an effective decline rate that is different from the dlim value. The stretched exponential decline method is a variation of the traditional Arps method, but is better suited to unconventional reservoirs due to its bounded nature.
The resource triangle, Figure 1, describes the distribution of original gas in place (OGIP) in a typical basin. At the top of the triangle are the high permeability reservoirs. These reservoirs are small, and, once discovered, as much as 80 to 90% of the OGIP can be produced using conventional drilling and completion methods. As we go deeper into the resource triangle, the permeability decreases, but the size of the resource increases. Higher gas prices and better technology are required to produce significant volumes of gas from these tight gas reservoirs.
Klie, Hector (DeepCast.ai) | Klie, Arturo (DeepCast.ai) | Rodriguez, Adolfo (OpenSim Technology) | Monteagudo, Jorge (OpenSim Technology) | Primera, Alejandro (Primera Resources) | Quesada, Maria (Primera Resources)
The Vaca Muerta formation in Argentina is emerging as one of the most promising resources of shale oil/gas plays in the world. At the current well drilling pace, challenges in streamlining data acquisition, production analysis and forecasting for executing timely and reliable reserves and resource estimations will be an overarching theme in the forthcoming years. In this work, we demonstrate that field operation decision cycles can be improved by establishing a workflow that automatically integrates the gathering of reservoir and production data with fast forecasting AI models.
We created a data platform that regularly extracts geological, drilling, completion and production data from multiple open data sources in Argentina. Data cleansing and consolidation are done via the integration of fast cross-platform database services and natural language processing algorithms. A set of AI algorithms adapted to best capture engineering judgment are employed for identifying multiple flow regimes and selecting the most suitable decline curve models to perform production forecasting and EUR estimation. Based on conceptual models generated from minimum available data, a coupled flow-geomechanics simulator is used to forecast production in other field areas where no well information is available. New data is assimilated as it becomes available improving the reliability of the fast forecasting algorithm.
In a matter of minutes, we are able to achieve high forecasting accuracy and reserves estimation in the Vaca Muerta formation for over eight hundred wells. This workflow can be executed on a regular basis or as soon as new data becomes available. A moderate number of high-fidelity simulations based on coupled flow and geomechanics allows for inferring production scenarios where there is an absence of data capturing space and time. With this approach, engineers and managers are able to quickly examine a feasible set of viable in-fill scenarios. The autonomous integration of data and proper combination of AI approaches with high-resolution physics-based models enable opportunities to reduce operational costs and improving production efficiencies.
The integration of physics-based simulations with AI as a cost/effective workflow on a business relevant shale formation such as Vaca Muerta seems to be lacking in current literature. With the proposed solution, engineers should be able to focus more on business strategy rather than on manually performing time-consuming data wrangling and modeling tasks.
Dommisse, Robin (University of Texas) | Janson, Xavier (University of Texas) | Male, Frank (University of Texas) | Price, Buddy (The University of Texas at Austin) | Payne, Simon (Ikon Science) | Lewis, Andrew (Fairfield Geotechnologies)
Modern reservoir characterization approaches can be greatly aided by incorporating all available data and interpretations in a three dimensional geomodel. Our goal is to offer a regional perspective to augment the interpretations from local, field-scale 3D models developed by the industry. In this work we highlight the benefits of continuous development of the geomodel for the characterization of the facies architecture of an unconventional play.We generated a three dimensional, faulted Delaware Basin geomodel, containing over 1 billion cells, including stratigraphic, petrophysical, core description, and production data for the Bone Spring and Wolfcamp intervals. The model is based on over 7,000 correlated wells, 650 wells with facies interpretations and approximately 9,000 horizontal production wells with analyzed decline curves and completion data. Additionally, a high-quality 3D seismic volume in the northeastern part of the Delaware Basin reveals the complex stratigraphic architecture of key producing intervals in the Permian Basin. The 3D volume, combined with regional 2D seismic lines, enabled refining the interpretation of the stratigraphic architecture of the Wolfcampian to Guadalupian shelf margin. This allows us to relate the slope to basin strata imaged in the 3D seismic to the well-established stratigraphic architecture of the surrounding platforms. The 3D seismic volume reveals the seismic geomorphology of several key intervals. There are two areas of focus: 1) Testing of the facies model derived from log and core analyses using different deterministic and stochastic attribute distribution techniques; and 2) Exploring the influence of geological trends on productivity. This work demonstrates the value of a multiscale, regional perspective to the practice of 3D reservoir characterization in the Delaware Basin.
With hundreds of rigs running and thousands of wells producing in unconventional plays, more and more data becomes available every day and it is ever more tempting to apply machine learning techniques for unconventional development, be it to identify geology sweet spot, understand performance drivers and optimize development strategies such as well spacing, completion and production designs etc. However, most of the previous applications of machine learning are limited to either certain types of data or small areas of interest. Consequently, the results often lack the predictability or generalizability necessary to impact important development decisions. We developed a flexible, scalable and integrated machine learning framework to leverage all sources of data for the goal of optimizing unconventional resources development.
The framework is built on a big data warehouse and on-demand capability to efficiently visualize and analyze large volumes of heterogeneous data. The most important pillar of the framework is the ability to transform all different types of data with fit-for-purpose methodologies to be closely related to the evaluation and prediction of well performance. This is enabled mechanistically by an interface to scripting languages such as R or Python for interactive data processing, validation and visualization. We also developed several innovative methodologies to overcome some common challenges in characterizing well performance and analyzing well spatial and temporal relationships in terms of well spacing, stacking and infill timing. Ultimately, all the data is regularized to be ready for machine learning. The framework enables a rich set of state-of-the-art machine learning techniques. More importantly, the integration of machine learning with geology, reservoir and development data in a visual environment enables very intuitive and interactive testing, validation and interpretation, which provides valuable insight and confidence for development decision making.
The framework has been extensively employed in Permian Basin for important technical studies such as evaluation of new formation, optimization of well completion and spacing, and even PUD reserve booking compatible with SPEE recommended reliable technology. Field case studies clearly demonstrate the applicability and efficiency of the framework as well as the predictability and insights the machine learning techniques offer.
Hydraulic fracturing is a well stimulation technique for improved natural gas production from tight gas and shale formations. However, the implementation of the technique brings in new formation damage considerations. During the fracturing treatment, a large volume of water is pumped with proppants into the well. Following the treatment, the well flows back. Only a small fraction of the injected water can be recovered. Over 75% of the injected volume, usually left unrecovered at the start of production that causes permeability damage and productivity impairment. Recently, in some shale formations, some operators have observed improved well performance after an extended shut-in period following initial flow-back of a well that is called the soaking process. This phenomenon is somewhat surprising as shutting in a well after hydraulic fracturing has been traditionally viewed as detrimental to production flow rates (imbibition and water block).
This paper will present field cases from different shale formation to investigate the soaking process effect on the well performance. There is still little data in the literature and different behavior for the effect of soaking on gas production from shale formations and the selection of soaking time is still arbitrary. Rate transient analysis (RTA) and decline curve analysis (DCA) were conducted on the production data before and after the soaking process to evaluate the change in the stimulated reservoir area. Finally, a new correlation for the improvement in gas production with the soaking process was proposed.
The main conclusion from the long shut-in is an increase in the gas flow rate and reduction in the water rate. Imbibition process during the well shut in spreads of the leaked fluid inside the reservoir far from the fracture interface. Hence, blocking by fracture water around the wellbore reduces and the gas mobility increases. Formation pressure gradient was found to be the more effective parameter on the well performance after soaking, followed by gas specific gravity which is an indication for the thermal maturity for the formation.