Both the computation of classical statistical measures (e.g., mean, mode, median, variance, standard deviation, and skewness), and graphic data representation (e.g., histograms and scatter plots) commonly are used to understand the nature of data sets in a scientific investigation--including a reservoir study. A distinguishing characteristic of earth-science data sets (e.g., for petroleum reservoirs), though, is that they contain spatial information, which classical statistical descriptive methods cannot adequately describe. Spatial aspects of the data sets, such as the degree of continuity--or conversely, heterogeneity--and directionality are very important in developing a reservoir model. Analysis of spatially rich data is within the domain of geostatistics (spatial statistics), but a foundation in classical statistics and probability is prerequisite to understanding geostatistical concepts. Sampling also has proved invaluable in thousands of studies, but it, too, can lead to statistical insufficiencies and biases.
Dynamic data is information that changes asynchronously as the information is updated. Unlike static data, which is infrequently accessed and unlikely to be modified, or streaming data, which has a constant flow of information, dynamic data involves updates that may come at any time, with sporadic periods of inactivity in between. In the context of reservoir engineering, dynamic data is used during the creation of a reservoir model in conjunction with historical static data. When modeled accurately, any sampling from the conditional distribution would produce accurate static and dynamic characteristics. When a permanence of ratio hypothesis is employed, the conditional probability P(AǀB,C) can be expressed in terms of P(A), P(AǀB), and P(AǀC).
You have access to this full article to experience the outstanding content available to SPE members and JPT subscribers. To ensure continued access to JPT's content, please Sign In, JOIN SPE, or Subscribe to JPT This paper presents how a US onshore operator took a three-step approach to optimize more than 100 rod-pump wells. The approach involved data consolidation, automated work flows, and interactive data visualization. This approach led to increased unit run times, decreased unit cycling, improved production and equipment surveillance, and increased staff productivity. The ultimate goal was to increase profitability by decreasing lifting costs and increasing operating efficiency.
In the traditional sequential workflow approach, the geomodeler builds static models based solely on log and core data interpretations, sometimes supplemented with geological understanding, without any dynamic data considerations. In the consequent step in the traditional workflow, the simulation engineer modifies the static model, as required, to achieve a match to the dynamic data, sometimes ending up with a modified geomodel that is significantly different from the original static geomodel. In the modern integrated reservoir modeling practice, the established workflows have become a cyclical process where learnings from the history match are taken back to refine the geomodel. For example, if a well does not produce its historical rate during history match, the permeability-thickness product (KH) around the well is caliberated to well-testing KH using pressure transient derivative matching and the discrepancy is taken back to the geomodel to be resolved. With the intent to reduce history match cycle time, different approaches have been developed to use underlying data input, e.g., seismic impedance, object-based geological features, pressure transient derivative signature or pressure stream lines, to constrain the geomodel 3-D property population to more realistic outcomes based on the geological understanding and available dynamic data. This publication proposes a new such approach: Pressure Conditioned Modeling (PCM). The PCM concept is based on grouping wells with similar time-lapse static reservoir pressure trends into the same Connected Reservoir Region (CRR).
PCM is based on the assumption that similarity of time-lapse shut-in reservoir pressure trends between wells in a reservoir is an indication that the producers are draining from same connected reservoir region (same CRR), and no large scale geomodel permeability barrier is allowed to exist between these wells. Time-lapse shut-in pressure data of all wells in the reservoir are grouped on the basis of similar trends. A CRR map is created to reflect the spatial distribution of the hydraulically connected wells. The geomodeler then uses this CRR map as input in the 3-D permeability variogram definition. The core permeability data existing within each CRR is distributed only inside the subject CRR in such a way that no undesirable intra-CRR permeability barrier occurs.
The PCM methodology imposes a connectivity range on 3-D permeability distribution thereby ensuring that the connected areas within a globally heterogeneous reservoir are properly designated. A synthetic model example discussed in this paper resulted in a better pre-modification history match of wells and hence would require less time for history matching. More realistic field development predictions would also be achieved due to the improved connectivity between injectors and producers within each CRR in a fashion consistent with the observed field data.
For reservoirs with different multiple distinct multi-well pressure trends in the existing production history, the PCM concept should be used as it will produce a higher quality initial geomodel and significantly reduce the time required to obtain a history matched model without the need for significant modifications.
Pad drilling has become commonplace for North America shale development drilling, which requires tighter well spacing/separation and reduced anti-collision risk. A new digitally-controlled rotary-steerable system (RSS), extensively embedded with electronics, solid-state sensors and electrically controlled mud valve, has been developed specifically for drilling vertical and nudge well profiles from pads in North America. Unique technology includes a slow-rotating steering housing with four mud activated pads to apply side force at the bit. The pad activation is controlled using a novel mud valve driven by a low-power electric motor and gearing system. Activation of the steering pads and control of force to the steering pads is achieved using a small percentage of mud flow and approximately 500 psi pressure drop below the tool. The limited amount of mud flow passing through the mud valve eliminates internal wash issues and reduces repair costs.
The electronics measurement and control system are mounted in the slow-rotating steering housing and includes 3-axis inclinometers, 3-axis magnetometers, 3-axis shock sensors, 3-axis gyros, and temperature sensors. Additionally, compact drilling dynamics sensors are placed at the bit box to gather at-bit data to evaluate bit-rock dynamic interaction.
This paper will describe the unique features that allow the system to be reliable and cost-effective for high-volume land drilling activities. The RSS bottom-hole assemblies (BHAs) have been extensively instrumented with multiple downhole dynamics sensors, which reveal a challenging drilling environment unique to vertical drilling and nudge applications and show the performance of the RSS in this environment.
Mahmoud (Mudi) Ibrahim and Gregor Hollmann, Wintershall Summary Brownfields in this paper are defined as mature fields where production declined to less than 35-40% of the plateau rate and where primary and secondary reserves have been largely depleted. Big data, high field complexity after a long production history, and slim economic margins are typical brownfield challenges. In the exploration-and-production (E&P) industry, "sequential" field-evaluation approaches (first "static," then "dynamic"), have proved successful for greenfield development, but often do not achieve satisfying results for brownfields. This paper presents a new work flow for brownfield reevaluation and rejuvenation. The "reversed" geo-dynamic field modeling (GDFM) rearranges existing elements of reservoir evaluation to obtain a purpose-driven, deterministic reservoir model, which can be quickly translated into development scenarios. The GDFM work flow is novel because (1) it turns upside down the discipline-driven sequential work flow (i.e., starts with the history match) and (2) it uses dynamic data as input to calibrate seismic (re-) interpretation that acts as a main integration step. It combines all available data already during horizon and fault mapping. Field diagnosis, flow-unit identification, well-test reanalysis, and petrophysical and geological interpretations are all combined in a cross-discipline interaction to guarantee data consistency. This directly ensures a fully integrated, "geo-dynamic" model that forms the basis for reservoir modeling.
PA & Simulation well indexes calibrated in situ from PTA grid 0.01 0.1 1 10 Raw Data Extracted Data Analyses (PTA PA HM) Filtered & Decimated Data Synchronized Data Proxy Model Human Process Reallocated Data ALARMS! Raw Data Extracted Data'some' kh & skin Filtered & Decimated Data Synchronized Data'some' proxy straight line Human Process'some' results'some' report Most technologies described here were initiated by research projects in the Petroleum Eng. Yesterday's investment in these projects turned out to be marginal compared to today's benefits…
The Main Pay reservoir of the supergiant Rumaila oil field in southeast Iraq has been on production since 1953, and is now in a mature production phase. An onshore field of this scale and longevity is in a rare position to benefit from large volumes of high density dynamic data, including repeat cased hole saturation logs, formation pressure data and production logging tool runs. In mature field life these data are beginning to highlight complex reservoir behaviour, not fully captured in previous subsurface descriptions. The Main Pay represents the maximum regression of the Early Cretaceous Zubair Formation; a paralic reservoir deposited in a delta-front setting. The Main Pay is divided into three reservoir units, in terms of both stratigraphic expression and dynamic performance, based on major mudstone-prone flooding surfaces.